Research ArticleLayout Design of Human-Machine Interaction Interface ofCabin Based on Cognitive Ergonomics and GA-ACA

Li Deng12 Guohua Wang3 and Suihuai Yu2

1School of Mechatronic Engineering Southwest Petroleum University Chengdu 610500 China2Institute of Industrial Design Northwestern Polytechnical University Xirsquoan 710072 China3State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation Southwest Petroleum University Chengdu 610500 China

Correspondence should be addressed to Li Deng dengliswpueducn

Received 11 September 2015 Revised 21 December 2015 Accepted 21 December 2015

Academic Editor Manuel Grana

Copyright copy 2016 Li Deng et al This is an open access article distributed under the Creative Commons Attribution License whichpermits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

In order to consider the psychological cognitive characteristics affecting operating comfort and realize the automatic layout designcognitive ergonomics andGA-ACA (genetic algorithm and ant colony algorithm)were introduced into the layout design of human-machine interaction interface First from the perspective of cognitive psychology according to the information processing processthe cognitive model of human-machine interaction interface was established Then the human cognitive characteristics wereanalyzed and the layout principles of human-machine interaction interface were summarized as the constraints in layout designAgain the expression form of fitness function pheromone and heuristic information for the layout optimization of cabin wasstudiedThe layout designmodel of human-machine interaction interfacewas established based onGA-ACAAt last a layout designsystem was developed based on this model For validation the human-machine interaction interface layout design of drilling rigcontrol roomwas taken as an example and the optimization result showed the feasibility and effectiveness of the proposedmethod

1 Introduction

Human-machine interaction interface is the medium be-tween human andmachine to transmit information and is thespecific formof expression between the humanmachine andenvironment which is also the necessary means to realize theinteraction In themodel of human-machine system throughthe visual and auditory organ people receive informationfrom machine then with the processing and decision inbrain the locomotive organ reacts to realize the informationtransmission between human and machine On the otherhand all sorts of machinersquos display have effect on peoplewhich will realize the information transmission betweenmachine and human

Cha et al [1] proposed that layout design problem wasan important part of the design field Layout design problemis to put objects in space reasonably to meet the necessaryconstraints and achieve some kind of optimal index [2] Thelayout problem involves bin packing problem strip packingproblem cutting stock problem office layout design [3]workshop layout design [4] and so on At present the solving

approach of layout problem is usually to simplify the practicalengineering problem into mathematical model and thenthrough computer algorithm to solve For example Huoet al [5] presented human-machine cooperation immunealgorithm ant colony algorithm genetic algorithm andother methods to solve the problem of satellite cabin layoutdesign and acquired the engineering satisfactory solution andsuperior computational efficiency Li et al [6] put forward theaircraft cockpit layout optimization designmethod includingthe layout of display and operating device Yan et al [7] putforward the optimizationmethod of controls layout based onsimulated annealing algorithm Zong et al [8 9] establishedthe layout optimization mathematical model of mannedsubmersibles cabin proposed multiobjective optimizationcalculation method based on the Pareto PGA algorithmand proposed artificial fish algorithm to solve the deepsubmergence cabin layout optimization problem Wang et al[10] established layout optimization mathematical model ofship cabin based on improved genetic algorithm

The layout problem of human-machine interaction inter-face was getting more and more inseparable with artificial

Hindawi Publishing CorporationComputational Intelligence and NeuroscienceVolume 2016 Article ID 1032139 12 pageshttpdxdoiorg10115520161032139

2 Computational Intelligence and Neuroscience

Parent individuals

Gene

Genetic variation

Environment

Optimal offspring

Initial individuals

Genotype

Selection crossover and

mutation operation

Fitness function

Optimal set

Output the optimal solution

(1) Establish the cognitive model of human-machineinteraction interface

(2) Summarize the layout principles of human-machineinteraction interface

(i) Cognition corresponds to objective

(ii) Task flow design(iii) Human-machine interaction interface

matches with the usersrsquo cognitive strategy(iv) In accordance with information

organization law

(3) Establish the layout design model of human-machine interaction interface

(i) The former process of algorithm GA(ii) The latter process of algorithm ACA

middot middot middot middot middot middotmiddot middot middot

middot middot middot

middot middot middot

middot middot middot

middot middot middot

middot middot middotmiddot middot middotmiddot middot middot middot middot middotS1

S11

S21 S22 S23

S33S32S31

S12 S13

S2

Sn1 Sn2 Sn3 Snn

Sn

S1n

S2n

S3n

Biological genetic Genetic algorithm Ant colony algorithm

Gene1 Gene2 Genen

Figure 1 The implementation steps

intelligence technology Researchers began to pay attentionto various computational intelligence algorithms such asgenetic algorithm particle swarm optimization algorithmsimulated annealing algorithm ant colony algorithm andtabu search Besides according to the NO Free Lunchtheorem proposed by Professors Wolpert and Macready inStanford University [11] a single optimization algorithmhad its advantages and disadvantages of application Fromthe perspective of solving optimization problem the fusionof different types of algorithmic mechanism and givingfull play to their respective advantages were the inevitabledevelopment trend to solve the problem Hybrid intelligentoptimization algorithm had become an important strategy tosolve practical engineering problems [12]

The effect of the algorithm in different applications wasdifferent so according to the characteristics of the cabinsolving algorithm should be suitable for the layout problemThe current layout research was focused on the improvementof space utilization [13] so the traditional optimization objec-tive was difficult to meet the requirements of human bodyrsquoscomfort in operating Human physiological and psychologi-cal characteristics should be considered in the layout designso as to make the operators have comfortable operating pos-tureThus from the perspective of cognitive psychology [14]the layout principles of human-machine interaction interfacewere summarized and the human cognitive characteristicswere quantified as the layout constraints GA-ACA [15] wouldbe applied to realize the intelligent layout optimization ofhuman-machine interaction interface of cabin

2 Implementation Method

21 Outline of the Proposed Method This paper defined thatthe cabin was a kind of semiclosed or totally enclosed spacemainly including aerospace manned cockpit engineeringmachinery cab automobile cab submersible manned cabinoil rig driller control room and nuclear power plant controlroom Through human-machine interaction interface theoperators proceededwith operation tasks in the interior spaceof cabin As the operatorsrsquo working environment the cabindirectly impacted on the operatorsrsquo working status Reason-able layout design of human-machine interaction interface

could improve the operatorsrsquo identification ability and workefficiency On the contrary it was possible that improperlayout design can lead to wrong operation and occupationaldisease and even affect the safety of the operating system

American cognitive psychologist Neisser defined thatcognitive psychology is to study allmental processes bywhichthe sensory input is transformed reduced elaborated storedrecovered and used [16] From the perspective of cognitiveergonomics human-machine interaction process was a cog-nitive process Interactive process was not only the feelingprocess of the physiological action and the stimulus signalbut also one kind of information processing process Thehuman-machine interaction interface layout design shouldadapt to peoplersquos understanding and operating process

The implementation steps of layout design method areshown in Figure 1

Step 1 (establish the cognitivemodel of human-machine inter-action interface) The reasons of cognitive errors could beanalyzed by the cognitive model which described thecognitive process Taking the improvement of cognitiveergonomics as the goal the cognitive theorywas used to guidethe layout design of human-machine interaction interface

Step 2 (summarize the layout principles of human-machineinteraction interface) Adhering to the idea of ldquoUser Cen-tered Designrdquo users were fully considered in the layoutdesign According to the analysis of cognitive ergonomicsthe layout principles of human-machine interaction interfacewere summarized on the basis of human cognitive character-istics

Step 3 (establish the layout design model of human-machineinteraction interface) Hybrid intelligent optimization algo-rithm was used to solve the combination optimal problemThe layout design model of human-machine interactioninterface was established based on GA-ACA which com-bined the advantages of GA and ACA

22 Establish the Cognitive Model of Human-Machine Interac-tion Interface Human-machine interaction process is actu-ally a process of information processing Through the

Computational Intelligence and Neuroscience 3

Machine Rig

DisplayInput

Stimulate

Receptor

Short-term sensory storage

Perception

Response implementation

Long-term memory

OutputManipulator

Decision-making

information

Attention

Working memory

HumanDriller

Intuitionlayer

Template layer

The constraint module including the operatorsrsquo subjective understanding habits and principle of information processing

Environment

Reasoning layer

Comparator

Visualhearing

and so forth

Perceptual activities

The driller control room

The human-machine interaction interface

Feedback

Figure 2 The cognitive model of human-machine interaction interface of cabin

characteristics about mental labor in cognitive psychol-ogy such as the research of memory understanding andcommunication the designed human-machine interactioninterface tries to reduce peoplersquos cognitive burden as much aspossible making the product easy to learn easy to use andwith high efficiency The idea of human-machine interactioninterface layout design was to build cognitive model basedon the information processing mechanism Then on thebasis of peoplersquos thinking characteristics using the rule ofinformation organization visual search pattern andmemorycharacteristic the human-machine interaction interface lay-outwould conform to operatorsrsquo cognitive ability for interfaceinformation

There were plenty of studies on cognitive processespsychologistsrsquo emphasis on the study of cognitive processeswas different at different periods Information processingmodel described the main elements or stages in the humaninformation processing and the hypothetical relationshipbetween them Most of the models were consistent with thisbasic framework And on this basis Wickens et al [17] putforward the model of information processing with the atten-tion function Sun et al [18] put forward cognitive syntheticmodel which brought cognitive process into the interac-tion between human and environment system according tothe thought of parallel processing of layered informationand output by competition and coordination Taking andintegrating the advantages of the predecessorrsquos research asreference this paper put forward the cognitive model ofhuman-machine interaction interface of cabin (shown inFigure 2)

As can be seen from Figure 2 cognitive processes areplaced in the interaction system of human machine andenvironment Through visual and auditory organ the oper-ators observe the systemrsquos operating situation and proceedwith sensory processing Combining call rules and knowl-edge in long-term memory and call targets and tasks inshort-term memory with constraint module the sensory

information processing is conducted Information process-ing includes intuition layer template layer reasoning layerand comparator Information is parallel-processed by thethree layers of intuition template and reasoning and theschemes are produced in the competition and cooperationin the three layers and output in the comparator Finallythe selected corresponding method is carried out Infor-mation acquisition processing and performing all need tointeract with the constraint module which is the operatorsrsquosubjective understanding habits and principle of informa-tion processing Perception decision-making and responseimplementation all need to consume attention In order toobtain the best cognitive ergonomics appropriate cognitivestrategies should be adopted to balance the quality and speedof the information processing in cognitive systemThismodelcould describe the operatorsrsquo cognitive process clearly so itis suitable for application in the layout design of human-machine interaction interface of cabin

23 Summarize the Layout Principles Based on CognitivePsychology The information processing is not only affectedby stimulation but also influenced by the past experienceand knowledge To improve the efficiency of human-machineinteractive information cognitive law involving the human-machine interaction interface layout design should be sum-marized whichwould be transformed into guiding principlesfor layout design [19 20]

Principle 1 (cognition corresponds to objective) Using thehuman-machine interaction interface in line with usersrsquoexperience and knowledge and adopting the appropriateprocessing method conform to the old habits and conceptswhich could reduce the learning time and memory timeand avoid the happening of mistakes For instance themanipulators with similar functions should be arranged inthe same area Because when the manipulators with similarfunctions needed to be used the attention of operators would

4 Computational Intelligence and Neuroscience

Principle 1 Cognition corresponds to objective

According to the functional partition

Principle 2 Task flow design

According to the operating sequence to arrange

Principle 3 Human-machineinteraction

interface matches with the usersrsquo cognitive

strategy

According to the importance and operating frequency to arrange

Principle 4 In accordance with

information organization law

According to the correlation to arrange

Arranged in one area when coding

Fitness function

Ti

Si

Oij

Figure 3 The process of building objective function

search the manipulator at the familiar area of interface in anautomatic way

Principle 2 (task flow design) Through the operatingsequence design to reduce the usersrsquo workload and improvethe working efficiency in view of the main tasks of theoperator that need to be done the execution process oftasks should be analyzed And then according to operatorsrsquocognitive habits to merge or reduce the unnecessary actionsor implement automation so as to simplify the dialogueprocess of human-machine interaction which would speedup the information processing and reduce the usersrsquo cognitiveload and cognitive time in information processing

Principle 3 (human-machine interaction interface matcheswith the usersrsquo cognitive strategy) Human cognition haddynamic characteristic the human-machine interactioninterface was very difficult to adapt to the dynamic cognitivedifferences of all kinds of users So based on usersrsquo knowledgelevel cognitive ability and habits meanwhile by judging thedifferent levels of usersrsquo cognitive strategy human-machineinteraction interface would be designed intelligently so as toadapt to the usersrsquo cognitive process dynamically For exam-ple according to the characteristics ofmemory the importantdisplay andmanipulator whichwould be frequently observedandmanipulated should be arranged in the convenient rangefor observation and comfortable range for manipulationTheburden on the usersrsquo short-term memory could be easedAnd it was advantageous to reasonably distribute cognitiveload between layers of intuition template and reasoning andavoiding forgetting and memory errors such as consequence

Principle 4 (in accordance with information organizationlaw) The discrete stimulate in view could be organizedtogether and formed the vision of a whole by the certainrelationship between them and this phenomenonwas knownas the visual organization features Visual organizationalprinciples mainly included proximity principle similarity

principle and closeness principle which had a certain guid-ing significance in the interface design For example supposethere was lots of information that needed to be displayed onthe screen in order tomake the information displayed clearlythe relevant information should be put together by proximityprinciple or be expressed in the same color by similarityprinciple So this information seemed to be whole and theusers could detect them rapidly and accurately

24 Determine the Optimization Objective according to theLayout Principles The human-machine interaction interfacelayout problem could be transformed into the combinatorialoptimization problem that is the layout scheme whichmost met the layout principles was sought from differentpermutation and combination scheme of the layout objectsThe optimization objectivewas to find the best layout schememaking the objective function get the maximum value Theprocess of building objective function is shown in Figure 3

In short layout Principle 1 embodied in the layout objectswith similar function will be arranged in one area LayoutPrinciple 2 is embodied in the arrangement in accordancewith the physical activities characteristics from left to rightand from top to bottom Layout Principle 3 embodied in theimportant layout objects will be arranged near the best layoutpoint 119896 Layout Principle 4 embodied in the related layoutobjects will be arranged close to each other

According to the layout principles the objective functionis defined as follows

119891 (119894)best

= argmax[

[

119899

sum

119894=1

1205751sdot 119879119894+

119899

sum

119894=1

1205752sdot 119878119894+

119899

sum

119894=1

119899

sum

119895=119894+1

119874119894119895

119889119894119895

]

]

(1)

1205751= 1 minus

119894

119899 (2)

1205752= 1 minus

119889119894119896

119889119896119898

(3)

Computational Intelligence and Neuroscience 5

SelectCross

Mutate

ttat0

AAGA

N+ 1

N + 1Define the target and the fitness function

Generate a number of optimal solutions

The layout design of human-machineinteraction interface

Output the optimal solution

Update the pheromone of each route

m ants traversal n nodes after the ants pass throughaccording to the optimal route to increase the pheromone

Calculate the probability of each ant moving to the next nodethen according to probability to move the ants to the next node

Initialization parameters take the optimal solution generated by GAas the distribution of initial pheromoneput m ants on n nodes

a

Figure 4 The process of GA-ACA

where 119879119894represents the weight of layout object 119894 relative

to Principle 2 119878119894represents the weight of layout object 119894

relative to Principle 3 119874119894119895represents the correlation between

layout object 119894 and layout object 119895 relative to Principle4 119889119894119895represents the distance between layout object 119894 and

layout object 119895 119899 represents the number of layout objects 119894represents the position number of layout objects 120575

1and 120575

2

represent the position control coefficients 119889119894119896represents the

distance between the layout object 119894 and the best layout point119896 119889119896119898

represents the maximum distance between a layoutpoint and the best layout point 119896 in the whole layout range

25 Establish the Layout Design Model of Human-MachineInteraction Interface Based on GA-ACA

251 GA-ACA In 1975 according to Darwinrsquos theory ofsurvival of the fittest the American scholar John Hollandput forward the genetic algorithm [21] Through parallel andglobal search mode to search the best individual in the opti-mization group GA had good adaptive ability robustnessgenerality and othermerits widely used inmachine learningpattern recognition image processing and other fields

In the early 1990s inspired from antsrsquo foraging behaviorin the nature world the Italian scholar Dorigo et al presentedant colony algorithm [22ndash24] ACA had very strong robust-ness and ability to search a good solution and easy to parallelimplementation Originally it is used to solve the travelingsalesman problem latter widely applied to solve the classicaloptimization problem such as sequential ordering problemand quadratic assignment problem

GA and ACA were stochastic optimization algorithmboth of which had the advantages of global search andrandom search and easily combined with other algorithmsHowever GA had the shortcomings of huge calculation andpoor stability leading to low precision and efficiency ACAneeded long search time and is easily prone to stagnationphenomenonThe combination of GA and ACA could utilizethe advantage of the two algorithms and overcome theirdisadvantages and the research has proved that the hybridalgorithm had good efficiency [25] This new algorithm wasused for multiple sequence alignment [26] initializationfor synchronous sequential circuits [27] hardwaresoftwarepartitioning [28] and other optimization problems

According to the study and experiment of GA and ACA[29] the speed-time curve is shown in Figure 4 In the earlystages of the search (119905

0sim 119905119886) GA has fast convergence When

evolution reaches a certain degree its evolutionary ratewouldfall sharply namely the efficiency is low after 119905

119886 Instead at

the beginning of the search (1199050sim 119905119886) ACA searches slowly

But after the accumulation of pheromone reaches a certainextent the searching activity of ants can present a certainregularity that is to say the speed is rapidly improved after119905119886As shown in Figure 4 this paper put forward combining

the advantages of the two algorithms in layout design GAis adopted in the former process of algorithm (before point119886) and the rapidity randomness and global convergence ofGA are used A number of layout schemes of human-machineinteraction interface are generated as initial solution whichwill be turned into track intensity distribution as initializationpheromone Then ACA is adopted to optimize the layoutschemes in the latter process of algorithm (after point119886) In the case of a certain track intensity distribution ofinitialization pheromone the characteristics of parallelismpositive feedbackmechanism and high solving efficiencywillbe used to improve the efficiency of solving and output theoptimal solution

252 GA to Determine the Initial Pheromone

(1) The Operating Mechanism of GA The process of GA inhuman-machine interaction interface layout is shown at theleft part in Figure 4

(a) Coding Using the form of sequence coding the layoutobjects were expressed by real number to carry out optimiz-ing calculation The code string format shows as follows

119862119898= [119888119898

1 119888119898

2 119888119898

3 119888

119898

119899] (4)

where 119898 represents the population size 119899 represents thenumber of the layout objects 119888119898

119899represents the layout object

in the 119899th position of the119898th chromosomeSuppose there are 10 layout objects which randomly

generated an individual such as [5 4 3 8 7 2 10 1 9 6]Thecode string represents layout object 5 in the first positionlayout object 4 in the second position and so on There are

6 Computational Intelligence and Neuroscience

direct relationships between the objective function and thecoordinate of layout objects If the codes of layout objectsare in different position the corresponding coordinatesare different and the objective function value will also bedifferent

(b) Construct the Fitness Function Take each kind of permu-tation and combination way as an individual and then thebest layout solution was sought by calculating the individualrsquosfitness value and evolutionary operationThe119901th fitness valueof chromosome is as follows

fitness (119901) =119899

sum

119894=1

1205751sdot 119879119894+

119899

sum

119894=1

1205752sdot 119878119894+

119899

sum

119894=1

119899

sum

119895=119894+1

119874119894119895

119889119894119895

(5)

The three parts of fitness function reflect the comprehen-sive situation of layout Principles 2 3 and 4 respectivelythe pros and cons of layout schemes will be judged from theoverall layout

(c) Determine the Evolutionary Mechanism It is done startingfrom the random generation of initial population constantlyrepeating the process of selection crossover and mutationoperation and the population developed along the directionof established goals from generation to generation Accordingto the fitness value of the fitness function excellent indi-viduals would be selected from the current population bywheel bet method so as to form a new population Usingsequence crossover method parent individuals exchangedpart of genes and thus new individuals would be formedUsing swapmutationmethod the diversity of populationwasguaranteed and immature convergence phenomenon wasprevented

(2) The Combination of GA and ACA The combinationopportunity of GA and ACA was dynamically determinedin the operational process of GA First set minimum geneticiterations Genmin andmaximum genetic iterations Genmax inGAThen according to formula (6) record the evolution rateof progeny population in the iterative process and set theminimumevolution rateGen

119901of progeny populationWithin

the scope of the given number of iterations if successiveGen119902generations the evolution rate of progeny population

were less than Gen119901 this showed that the optimization speed

was very low The process of GA should be terminated andturned into the ACA The 10 of the optimal individuals inlast generation in GA would be selected as the carrier whichwould be taken as the suboptimal solution to distribute theinitial pheromone in ACA and optimal solution would beobtained further

EV119896 =sum119898

119894=1(119891119896

119894minus 119891119896minus1

119894) 119891119896minus1

119894

119898 119896 = 2 3 119899 (6)

where 119898 represents population size 119896 represents evolutionalgeneration 119891119896

119894represents the fitness value of individual 119894 in

119896th iteration

253 ACA to Solve Pareto Solution The purpose of layoutoptimization of human-machine interaction interface was to

get an optimal solution to satisfy the design goal which wasto find an optimal path from layout object 119894 to layout object119895 and the optimized goal was to get a maximum of (1) Inthe ACA system model each feasible solution represented apath that an ant walked by It is a shortest path problem sothe objective function of ACA was defined as the reciprocalof the objective function of GA

119871119896=

1

119891 (119894)best (7)

(1) State Transition Rules The process of ACA in human-machine interaction interface layout is shown at the right partin Figure 4 Take the layout of manipulators to describe therealization process of ACA [30 31] Set 119887

119894(119905) (119894 = 1 2 119899)

represents the number of ants of manipulator 119894 in time 119905119898 =

sum119899

119894=1119887119894(119905) represents the total number of ants and 119899 represents

the number of manipulators Set tabu119896represents the tabu

table of ant 119896 which will be adjusted dynamically along withthe ant optimization process In initial stage 119898 ants willbe placed on 119899 manipulators randomly The first elementof each antrsquos tabu table will be set as its first manipulatorIn the process of each iteration each ant chooses the nextmanipulator by the state transition rules repeatedly Afterselection for 119899minus1 times a group ofmanipulators layoutwill begenerated eventually The probability of ant 119896 transfers frommanipulator 119894 to manipulator 119895 in time 119905 is as follows

119901119896

119894119895(119905) =

[120591119894119895(119905)]120572

[120578119894119895]120573

sum119897isin119880

[120591119894119897(119905)]120572

[120578119894119897]120573

119895 isin 119880

0 119895 notin 119880

120578119894119895=

1

V119894119895

V119894119895=10038161003816100381610038161003816119879119894minus 119879119895

10038161003816100381610038161003816+10038161003816100381610038161003816119878119894minus 119878119895

10038161003816100381610038161003816+10038161003816100381610038161003816119874119894minus 119874119895

10038161003816100381610038161003816

(8)

where 120591119894119895(119905) represents the pheromone between manipulator

119894 and manipulator 119895 at 119905 moment 120578119894119895represents the visibility

(heuristic information) of transferring from manipulator 119894to manipulator 119895 V

119894119895represents the difference value of com-

prehensive weight between manipulator 119894 and manipulator119895 to the layout principles 120572 (120572 ge 0) represents the relativeimportance of 120591 120573 (120573 ge 0) represents the relative importanceof 120578 119880 represents the feasible point set namely the set ofmanipulators can be chosen by ant 119896 at 119905moment

(2) Pheromone andHeuristic InformationThis paper adoptedthe max-min ant system (MMAS) which was proposed bythe Belgium scholar Thomas Due to the combined withGA initialized pheromone was different fromMMAS At theinitial moment initial pheromone value is set as follows

120591119894119895(0) = 120591

119862+ 120591119866 (9)

where 120591119862is the given pheromone constant based on the

specific scale of solution equivalent to the 120591min in MMAS120591119866represents the value of pheromone converted from the

solution of GA

Computational Intelligence and Neuroscience 7

Table 1 Partition coding for manipulators

The name of the manipulator Code The name of the manipulator Code

Range 1

E-BRAKE 1

Range 3

LEFT CATHEAD 9GEN E-STOP 2 RIGHT CATHEAD 10VFD E-STOP 3 ROTARY CATHEAD 11

PARKING BRAKE 4 ALARM 12

Range 2

RT MOTOR CONTROL 5

Range 4

AIR SPINNER 13RT INERTIA BRAKE 6 TOOLS SELECT 14RT SPEED SETTING 7 SPARE1 15RT TORQUE LIMIT 8 SPARE2 16

Ant chose next manipulator mainly according topheromone 120591

119894119895(119905) and heuristic information 120578

119894119895 120578119894119895

wasgiven by the layout problem to be solved and affected by V

119894119895

which remained unchanged during the operation processin algorithm Ants released pheromone on the path theypassed by In order to avoid too much residual informationto submerge heuristic information residual informationshould be upgraded After ants traversed through all themanipulators the pheromone updated in the environmentThe update equation is as follows

120591119894119895(119905 + 1) = (1 minus 120588) sdot 120591

119894119895(119905) + Δ120591

119894119895(119905)

Δ120591119894119895(119905) = sumΔ120591

119896

119894119895(119905)

Δ120591119896

119894119895(119905) =

119876

119871119896

(ant 119896 passes though (119894 119895))

0 (otherwise)

(10)

where 120588 (0 le 120588 le 1) represents the volatilization coefficientof pheromone Δ120591119896

119894119895(119905) represents the amount of pheromone

released between manipulator 119894 and manipulator 119895 by ant 119896in this cycle Δ120591

119894119895(119905) represents the increment of pheromone

between manipulator 119894 and manipulator 119895 after this cycle 119876is a constant which represents the amount of pheromone 119871

119896

represents the objective function value corresponding withthe layout of all the manipulators formed by ant 119896 traversedthrough in this cycle

Only the ant with optimal layout scheme could updatepheromone in one cycle The amount of pheromone on eachpath would be limited in scope of [120591min 120591max] If beyondthis range the pheromone would be mandatory set as 120591minor 120591max Compared with the standard ACA the improvedalgorithm prevented premature stagnation With all theants completing the traverse of all the manipulators thepheromone accumulated and volatilized continuously Untilreaching the number of iterations or a certain fitness valuethe optimal path formed by ants was the best layout scheme

3 Application Verification

31 Layout Design of Driller Control Room on Drilling RigTake the layout design of human-machine interaction inter-face of driller control room on drilling rig as an exampleto illustrate the proposed method The 16 manipulators of

Table 2 The size of each type of manipulators (mm)

The types of manipulators Size The codes of manipulatorsSwitch knobs Φ30 4 5 6 9 10 11 12 13 14 15 16Rotary knobs Φ50 7 8Buttons Φ40 1 2 3

ZJ1209000DB rig console would be arranged (the grey areaas shown in Figure 5) First of all the 16 manipulators needto be encoded As shown in Table 1 when encoding layoutPrinciple 1 needs to be reflected that is manipulators withsimilar function will be arranged in one area to reduce thesearch time for operators

It is necessary to simplify the layout area and layoutobjects when describing themathematical model to facilitatea digital description of design variables According to thehuman upper limb dimension to determine the layout spaceand simplify the layout area as rectangular area (450 times

400mm) there are three types of manipulators includingbuttons rotary knobs and switch knobs Their sizes areslightly different and the specific sizes are shown in Table 2The spaces between manipulators are set as equal and theinterval is 100mm After being simplified the layout ofmanipulators can be regarded as a scheduling problem firstusing GA-ACA to find a good sorting and then according tothe order to establish the actual layout

Drilling process is mainly divided into three workingconditions pull out of hole run in hole and normal drillingConsidering the principle of operating sequence in multipleworking conditions determine the manipulatorsrsquo operatingsequence and calculate each manipulatorrsquos weight relativeto Principle 2 Through questionnaires to consult drillersdetermine the relative judgment matrix of the manipulatorsrsquoimportance and frequency and calculate each manipulatorrsquosweight relative to Principle 3 Analytic hierarchy process willbe used to calculate each manipulatorrsquos weight of 119879

119894and

119878119894 and the calculation result is shown in Table 3 Finally

determine the relative correlation between the manipulatorsThe correlation between manipulator and itself is expressedwith 1 and the correlation between themanipulator and othermanipulators is expressed in decimal within 0sim1 The greaterthe correlation between manipulators the greater the corre-lation value The correlation between the 16 manipulators isshown in Table 4

8 Computational Intelligence and Neuroscience

Table 3 The manipulatorsrsquo weight relative to Principles 2 and 3

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16119879119894

0018 0018 0018 0055 0113 0076 0205 0163 0068 0068 0068 0016 0016 0098 0 0119878119894

0163 0115 0115 0115 0069 0069 0069 0069 0043 0043 0043 0013 0026 0026 0011 0011

LEFT DRUMSHIFT LOCK

AIR SLIPS

LEFT DRUMCLUTCH

AIR HORN

AD MODE AD MOTOR

LEFT AD LEFT ADCLUTCH

TOP SCREENWIPER CONTROL SPEED SETTING

PROTECTION RESET

SILIENCERESET

DW SPEEDCONTROL

KEYBOARD12 13 3 2 9

101 4

5 7

6 8

11 14

15 16

PARKINGBRAKE

RT INERTIABRAKE

RT SPEEDSETTING

TOOLSSELECT

E-BRAKE

VFDE-STOP E-STOPGEN

RT MOTORCONTROL

RT TORQUELIMIT

LEFT

RIGHTCATHEAD

CATHEAD

ROTARYCATHEAD

ALARM

AIRSPINNER

SPARE2SPARE1

Armrest

WORKINGBRAKERIGHT DRUM

SHIFT LOCKRIGHT DRUMCLUTCH

RIGHT ADSHIFT LOCH

SHIFT LOCH

RIGHT ADCLUTCH

FRONT SCREENWIPER CONTROL

Number 2 PUMP

Number 1 PUMP Number 3 PUMP

SPEED SETTING

SPEED SETTING

Figure 5 Layout schematic diagram of ZJ1209000DB rig console (before optimization)

32 Optimization Result After completing the above datapreparation as shown in Figure 6 the operating parametersof GA and ACA need to be set up before the computeraided optimization calculation proceeds First set the controlparameters of GA population size 119873 = 50 crossover rate119901119888= 06 mutation rate 119901

119898= 02 and number of partition

119904 = 4 Second set the control parameters ofACAThe amountof ants is119898 = 10 120572 = 1 120573 = 1 and 120588 = 05

In the system of GA-ACA calculation module set theend condition of GA minimum genetic iterations Genmin =

15 maximum genetic iterations Genmax = 50 minimumevolution rate Gene

119901= 3 and Gene

119902= 3 Set the control

parameters of pheromone 119876 = 500 120591min = 10 and 120591max =

100 The optimal 10 of individuals in the last generationin GA would be selected as the solution set of geneticoptimization which would be transformed into pheromone

values If manipulator 119894 was adjacent to manipulator 119895 ingenetic optimization solution the pheromone added 10 onthe path (119894 119895) and all the initial values of pheromonesshould be set like this When meeting one of the followingconditions theACA should stop (1)Thenumber of iterationsreaches antmax = 50 (2) For continuous three generations inthe iteration the improvement rate of offspring optimizationis less than 05

Click the button of algorithm running on the systeminterface the system calculates via the program after 50times genetic iterations and 11 times ant colony optimizationiteration the value of the optimal layout scheme is 2822The sequence of the manipulators is obtained that is 4 13 2 6 5 7 8 9 10 12 11 14 13 15 and 16 Accordingto the optimized sequence the layout scheme is shown inFigure 7

Computational Intelligence and Neuroscience 9

Table 4 The correlation between the 16 manipulators

119874119894119895

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 161 1 05 07 09 0 0 0 0 0 0 0 0 0 0 0 02 05 1 09 05 0 0 0 0 0 0 0 0 0 0 0 03 07 09 1 07 0 0 0 0 0 0 0 0 0 0 0 04 09 05 07 1 0 0 0 0 0 0 0 0 0 0 0 05 0 0 0 0 1 09 07 09 0 0 0 0 0 0 0 06 0 0 0 0 09 1 05 07 0 0 0 0 0 0 0 07 0 0 0 0 07 05 1 09 0 0 0 0 0 0 0 08 0 0 0 0 09 07 09 1 0 0 0 0 0 0 0 09 0 0 0 0 0 0 0 0 1 09 07 05 0 0 0 010 0 0 0 0 0 0 0 0 09 1 07 05 0 0 0 011 0 0 0 0 0 0 0 0 07 07 1 05 0 0 0 012 0 0 0 0 0 0 0 0 05 05 05 1 0 0 0 013 0 0 0 0 0 0 0 0 0 0 0 0 1 09 03 0314 0 0 0 0 0 0 0 0 0 0 0 0 09 1 03 0315 0 0 0 0 0 0 0 0 0 0 0 0 03 03 1 0916 0 0 0 0 0 0 0 0 0 0 0 0 03 03 09 1

Table 5 The comparison between GA ACA and ACA

Algorithm Layout scheme The value of layout scheme Number of iterationsGA 4 2 3 1 7 8 6 5 12 11 9 10 13 14 15 16 2814 82ACA 4 1 2 3 5 6 7 8 9 10 11 12 15 16 14 13 2773 75GA-ACA 4 1 2 3 7 8 5 6 12 9 10 11 15 16 13 14 2835 50 + 13

Figure 6 System interface of layout design

Because the proposed method is still in the research anddevelopment there is certain difference between the solutionand the actual situation Besides the human cognitive activ-ities and layout experience cannot be completely describedby the layout model layout scheme obtained by algorithmoptimization is close to the optimal solution rather than theoptimal solution In this stage the designer needs to adjustthe layout scheme according to the actual needs Similarlysort the manipulators on the console at the left hand Forthe sake of evaluating layout scheme intuitively computersimulation design modeling is proceeded Through detaileddesign the final layout scheme is shown in Figure 8

The CATIA software will be used to evaluate the resultof the layout CATIA V5 integrates four ergonomic moduleswhich can evaluate visibility accessibility the comfort ofposture (analyze the angle of each joint) and so on The bluearea in Figure 9 shows the reach envelope of drillerrsquos righthand Under natural state the drillerrsquos arm can operate themain manipulators on console This shows that the locationsof manipulators were in the human bodyrsquos correspondingrange of joint motion and in accord with the armrsquos motiontrail In Figure 10 the virtual human model simulates oneoperating posture of driller And the postural score analysisin Figure 11 shows the comfort score of lumbar vertebrathoracic vertebra head arm and forearmThe sores indicatethat this operating posture is comfortable the position of thismanipulator matches with the movement characteristics ofthe human body

33 Algorithm Comparison Using single GA ACA and GA-ACA hybrid algorithm to solve the above layout optimizationproblem respectively set the number of test times as 10each Result comparison is shown in Table 5 the columnof layout scheme shows the optimal result in 10 timesrsquorunning and the third and the fourth columns show theaverage value of layout scheme and the average number ofiterations As can be seen from the test result no matterthe solution speed or precision the GA-ACA is superiorto single GA and ACA in solving layout optimizationproblem

10 Computational Intelligence and Neuroscience

GEN E-STOPVFDE-STOP

LEFT RIGHT

E-BRAKEPARKINGBRAKE

RT INERTIABRAKE

RT SPEEDSETTING LIMIT

RT MOTORCONTROL

RT TORQUE ROTARYCATHEAD

CATHEAD CATHEAD

AIR SPARE1 SPARE2

ALARM

TOOLSSELECT

WORKINGBRAKE

Armrest0

200

0 200 400 600 800 10002004006008001000

400

600

800

Comfortableoperating range

operating rangeMaximum

1 3 24

56

7 8

9 10

1112

1314 15 16

SPINNER

Range 1

Range 2 Range 3

Range 4

y

x

Figure 7 Layout scheme of right console (after optimization)

Figure 8 Modeling and simulation of layout scheme

Figure 9 Analyze the right handrsquos reach envelope

34 Discussion Through reference to previous researchabout cognitive model this paper integrated their advantagesand put forward the cognitive model of human-machineinteraction interface suitable for cabin This model describedthe cognitive process of operators working in the cabinAnd then by analyzing the information processing processthe layout principles of human-machine interaction interfacewere summed up which would be quantified in the objectivefunction However human cognitive behavior process is verycomplicated part of the cognitive activities is difficult toexpress by formalized methods layout principles needed tobe improved further There were lots of influence factors in

Figure 10 The simulation of operating posture

Figure 11 The comfort evaluation of operating posture

actual engineering problem the layout problem was difficultto be described completely by mathematical model

It has become common that the bionic intelligencealgorithms were applied in the layout optimization designbut cognitive characteristics were seldom considered as theconstraints of layout optimization algorithm FurthermoreGA-ACA combined the advantages of two algorithms whichwould be more precise to solve layout optimization problemThe layout design of human-machine interaction interfaceof driller control room on drilling rig has illustrated theproposed method According to the characteristics of thehuman-machine interaction interface of different product

Computational Intelligence and Neuroscience 11

this method could be modified and adapted As a layoutergonomic design method it could assist designers andengineers to conduct human-machine interaction interfacedesign and improve the efficiency in layout design

4 Conclusion

Based on the theory of cognitive psychology according toWickens information processing model the cognitive modelof human-machine interaction interface was establishedConsidering peoplersquos information organization law visualsearch law user memory characteristics and so on thehuman-machine interaction interface should conform tooperatorsrsquo cognitive ability for interface information Basedon the cognitive model the layout principles of human-machine interaction interface were summarized as the layoutconstraintsThe problem of solving layout scheme was trans-formed into combinatorial optimization problem and GA-ACA was put forward to solve the problem which realizedthe algorithmization of artificial optimization process

Taking the layout of manipulators as example the objec-tive function of layout optimization was built according toeach principle Use GA to generate the solution of layoutscheme which would be transformed into initial pheromoneas ACA required Taking the difference value of comprehen-sive weight of the manipulators for the layout principles asheuristic information ofACA combinedwith the pheromoneprovided by GA the positive feedback optimization mecha-nism of ACA was used to solve further GA-ACA integratedthe complementary advantages of GA and ACA which hadgood optimizing performance and time performance andimproved the design efficiency Taking the 16 manipulators ofZJ1209000DB rig console as layout example layout designsystem of human-computer interaction interface for cabinwas developed by Visual Basic which validated the abovelayout optimization method

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgments

This research was supported by Open Fund (OGE201403-23) of Key Laboratory of Oil amp Gas Equipment Ministryof Education (Southwest Petroleum University) and by theOpen Research Subject (GY-14YB-31) of Key Laboratory(Research Base) of Industrial Design Industry Research Cen-ter Humanities and Social Science EducationDepartment ofSichuan Province

References

[1] J Z Cha X J Tang and Y P Lu ldquoSurvey on packing problemsrdquoJournal of Computer-AidedDesignampComputer Graphics vol 14pp 705ndash711 2002

[2] K A Dowsland and W B Dowsland ldquoPacking problemsrdquoEuropean Journal of Operational Research vol 56 no 1 pp 2ndash14 1992

[3] S Margaritis and NMarmaras ldquoSupporting the design of officelayout meeting ergonomics requirementsrdquo Applied Ergonomicsvol 38 no 6 pp 781ndash790 2007

[4] N Shariatzadeh G Sivard and D Chen ldquoSoftware evalu-ation criteria for rapid factory layout planning design andsimulationrdquo in Proceedings of the 45th CIRP Conference onManufacturing Systems pp 299ndash304 Athens GreeceMay 2012

[5] J Z Huo G Q Li H F Teng and Z G Sun ldquoHuman-computercooperative ant colonygenetic algorithm for satellite modulelayout designrdquo Chinese Journal of Mechanical Engineering vol41 no 3 pp 112ndash116 2005

[6] R Li D M Zhuang R Wang and L R Wang ldquoOptimizationabout the layout work of the steering arrangement in thecockpitrdquo Journal of System Simulation vol 16 pp 1305ndash13072004

[7] S Y Yan Y Chen and L Y Liang ldquoOptimization of controlslayout based on simulated annealing algorithmrdquo Nuclear PowerEngineering vol 35 no 1 pp 67ndash70 2014

[8] L C Zong C Ye S H Yu and D K Chen ldquoResearchand application of intelligent layout method in DSV cabinequipmentrdquo Shipbuilding of China vol 54 no 3 pp 147ndash1542013

[9] L C Zong S H Yu J B Sun L W Han and S S An ldquoStudyon cabin layout optimization with fish algorithmrdquo MechanicalScience and Technology for Aerospace Engineering vol 33 pp257ndash262 2014

[10] Y L Wang C Wang Z S Ji and X G Zhao ldquoA study onintelligent layout design of ship cabinrdquo Shipbuilding of Chinavol 54 pp 139ndash146 2013

[11] D HWolpert andWGMacready ldquoNo free lunch theorems foroptimizationrdquo IEEE Transactions on Evolutionary Computationvol 1 no 1 pp 67ndash82 1997

[12] F Q Zhao G Q Li C Yang A Abraham and H B LiuldquoA human-computer cooperative particle swarm optimizationbased immune algorithm for layout designrdquo Neurocomputingvol 132 pp 68ndash78 2014

[13] A Lodi S Martello and M Monaci ldquoTwo-dimensionalpacking problems a surveyrdquo European Journal of OperationalResearch vol 141 no 2 pp 241ndash252 2002

[14] J R AndersonCognitive Psychology and Itrsquos Implications editedby Y L Qin Posts amp Telecom Press 2012

[15] A Ghanbari S M R Kazemi F Mehmanpazir and M MNakhostin ldquoA cooperative ant colony optimization-geneticalgorithm approach for construction of energy demand fore-casting knowledge-based expert systemsrdquo Knowledge-BasedSystems vol 39 pp 194ndash206 2013

[16] J B Best Cognitive Psychology X T Huang Ed China LightIndustry Press Beijing China 2000

[17] C Wickens J G Hollands S Banbury and R ParasuramanEngineering Psychology amp Human Performance Pearson Lon-don UK 4th edition 2012

[18] L Y Sun Z X Li and T S Jin ldquoCognitive synthetic model andits application in HCIrdquo Journal of Xirsquoan Jiaotong University vol31 pp 74ndash80 1997

[19] Y P Zhang ldquoHuman-computer interface design based onknowledge of cognitive psychologyrdquo Computer Engineering andApplications vol 30 pp 105ndash107 2005

12 Computational Intelligence and Neuroscience

[20] Z Lan ldquoThe cognitive basis of software interface designrdquo ShanxiScience and Technology no 3 pp 41ndash42 1998

[21] X P Wang and L M Cao Genetic AlgorithmmdashTheory Appli-cation and Software Implementation Xirsquoan Jiaotong UniversityPress Xirsquoan China 2002

[22] E Bonabeau M Dorigo and G Theraulaz ldquoInspiration foroptimization from social insect behaviourrdquoNature vol 406 no6791 pp 39ndash42 2000

[23] M Dorigo and L M Gambardella ldquoAnt colonies for thetravelling salesman problemrdquo BioSystems vol 43 no 2 pp 73ndash81 1997

[24] MDorigo VManiezzo andA Colorni ldquoAnt system optimiza-tion by a colony of cooperating agentsrdquo IEEE Transactions onSystems Man and Cybernetics Part B Cybernetics vol 26 no1 pp 29ndash41 1996

[25] J-L Ding Z-Q Chen and Z-Z Yuan ldquoOn the Markovconvergence analysis for the combination of genetic algorithmand ant algorithmrdquo Acta Automatica Sinica vol 30 no 4 pp629ndash634 2004

[26] Z-J Lee S-F Su C-C Chuang and K-H Liu ldquoGeneticalgorithmwith ant colony optimization (GA-ACO) formultiplesequence alignmentrdquo Applied Soft Computing vol 8 no 1 pp55ndash78 2008

[27] Z Li C P Xu W Mo and G J Chen ldquoInitialization forsynchronous sequential circuits based on ant algorithm ampgenetic algorithmrdquo Acta Electronica Sinica vol 31 pp 1276ndash1280 2003

[28] Z-H Xiong S-K Li and J-H Chen ldquoHardwaresoftware par-titioning based on dynamic combination of genetic algorithmand ant algorithmrdquo Journal of Software vol 16 no 4 pp 503ndash512 2005

[29] J-L Ding Z-Q Chen and Z-Z Yuan ldquoOn the combinationof genetic algorithm and ant algorithmrdquo Journal of ComputerResearch and Development vol 40 no 9 pp 1351ndash1356 2003

[30] T Stutzle and H H Hoos ldquoMAX-MIN ant systemrdquo FutureGeneration Computer Systems vol 16 no 8 pp 889ndash914 2000

[31] T Stuetzle and H Hoos ldquoMAX-MIN Ant System and localsearch for the traveling salesman problemrdquo in Proceedings ofthe IEEE International Conference on Evolutionary Computation(ICEC rsquo97) pp 309ndash314 Indianapolis Indiana April 1997

Submit your manuscripts athttpwwwhindawicom

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Applied Computational Intelligence and Soft Computing

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HindawithinspPublishingthinspCorporationhttpwwwhindawicom Volumethinsp2014

Advances inSoftware EngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Electrical and Computer Engineering

Journal of

Journal of

Computer Networks and Communications

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporation

httpwwwhindawicom Volume 2014

Advances in

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International Journal of

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ArtificialNeural Systems

Advances in

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RoboticsJournal of

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Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Computational Intelligence and Neuroscience

Industrial EngineeringJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Human-ComputerInteraction

Advances in

Computer EngineeringAdvances in

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

2 Computational Intelligence and Neuroscience

Parent individuals

Gene

Genetic variation

Environment

Optimal offspring

Initial individuals

Genotype

Selection crossover and

mutation operation

Fitness function

Optimal set

Output the optimal solution

(1) Establish the cognitive model of human-machineinteraction interface

(2) Summarize the layout principles of human-machineinteraction interface

(i) Cognition corresponds to objective

(ii) Task flow design(iii) Human-machine interaction interface

matches with the usersrsquo cognitive strategy(iv) In accordance with information

organization law

(3) Establish the layout design model of human-machine interaction interface

(i) The former process of algorithm GA(ii) The latter process of algorithm ACA

middot middot middot middot middot middotmiddot middot middot

middot middot middot

middot middot middot

middot middot middot

middot middot middot

middot middot middotmiddot middot middotmiddot middot middot middot middot middotS1

S11

S21 S22 S23

S33S32S31

S12 S13

S2

Sn1 Sn2 Sn3 Snn

Sn

S1n

S2n

S3n

Biological genetic Genetic algorithm Ant colony algorithm

Gene1 Gene2 Genen

Figure 1 The implementation steps

intelligence technology Researchers began to pay attentionto various computational intelligence algorithms such asgenetic algorithm particle swarm optimization algorithmsimulated annealing algorithm ant colony algorithm andtabu search Besides according to the NO Free Lunchtheorem proposed by Professors Wolpert and Macready inStanford University [11] a single optimization algorithmhad its advantages and disadvantages of application Fromthe perspective of solving optimization problem the fusionof different types of algorithmic mechanism and givingfull play to their respective advantages were the inevitabledevelopment trend to solve the problem Hybrid intelligentoptimization algorithm had become an important strategy tosolve practical engineering problems [12]

The effect of the algorithm in different applications wasdifferent so according to the characteristics of the cabinsolving algorithm should be suitable for the layout problemThe current layout research was focused on the improvementof space utilization [13] so the traditional optimization objec-tive was difficult to meet the requirements of human bodyrsquoscomfort in operating Human physiological and psychologi-cal characteristics should be considered in the layout designso as to make the operators have comfortable operating pos-tureThus from the perspective of cognitive psychology [14]the layout principles of human-machine interaction interfacewere summarized and the human cognitive characteristicswere quantified as the layout constraints GA-ACA [15] wouldbe applied to realize the intelligent layout optimization ofhuman-machine interaction interface of cabin

2 Implementation Method

21 Outline of the Proposed Method This paper defined thatthe cabin was a kind of semiclosed or totally enclosed spacemainly including aerospace manned cockpit engineeringmachinery cab automobile cab submersible manned cabinoil rig driller control room and nuclear power plant controlroom Through human-machine interaction interface theoperators proceededwith operation tasks in the interior spaceof cabin As the operatorsrsquo working environment the cabindirectly impacted on the operatorsrsquo working status Reason-able layout design of human-machine interaction interface

could improve the operatorsrsquo identification ability and workefficiency On the contrary it was possible that improperlayout design can lead to wrong operation and occupationaldisease and even affect the safety of the operating system

American cognitive psychologist Neisser defined thatcognitive psychology is to study allmental processes bywhichthe sensory input is transformed reduced elaborated storedrecovered and used [16] From the perspective of cognitiveergonomics human-machine interaction process was a cog-nitive process Interactive process was not only the feelingprocess of the physiological action and the stimulus signalbut also one kind of information processing process Thehuman-machine interaction interface layout design shouldadapt to peoplersquos understanding and operating process

The implementation steps of layout design method areshown in Figure 1

Step 1 (establish the cognitivemodel of human-machine inter-action interface) The reasons of cognitive errors could beanalyzed by the cognitive model which described thecognitive process Taking the improvement of cognitiveergonomics as the goal the cognitive theorywas used to guidethe layout design of human-machine interaction interface

Step 2 (summarize the layout principles of human-machineinteraction interface) Adhering to the idea of ldquoUser Cen-tered Designrdquo users were fully considered in the layoutdesign According to the analysis of cognitive ergonomicsthe layout principles of human-machine interaction interfacewere summarized on the basis of human cognitive character-istics

Step 3 (establish the layout design model of human-machineinteraction interface) Hybrid intelligent optimization algo-rithm was used to solve the combination optimal problemThe layout design model of human-machine interactioninterface was established based on GA-ACA which com-bined the advantages of GA and ACA

22 Establish the Cognitive Model of Human-Machine Interac-tion Interface Human-machine interaction process is actu-ally a process of information processing Through the

Computational Intelligence and Neuroscience 3

Machine Rig

DisplayInput

Stimulate

Receptor

Short-term sensory storage

Perception

Response implementation

Long-term memory

OutputManipulator

Decision-making

information

Attention

Working memory

HumanDriller

Intuitionlayer

Template layer

The constraint module including the operatorsrsquo subjective understanding habits and principle of information processing

Environment

Reasoning layer

Comparator

Visualhearing

and so forth

Perceptual activities

The driller control room

The human-machine interaction interface

Feedback

Figure 2 The cognitive model of human-machine interaction interface of cabin

characteristics about mental labor in cognitive psychol-ogy such as the research of memory understanding andcommunication the designed human-machine interactioninterface tries to reduce peoplersquos cognitive burden as much aspossible making the product easy to learn easy to use andwith high efficiency The idea of human-machine interactioninterface layout design was to build cognitive model basedon the information processing mechanism Then on thebasis of peoplersquos thinking characteristics using the rule ofinformation organization visual search pattern andmemorycharacteristic the human-machine interaction interface lay-outwould conform to operatorsrsquo cognitive ability for interfaceinformation

There were plenty of studies on cognitive processespsychologistsrsquo emphasis on the study of cognitive processeswas different at different periods Information processingmodel described the main elements or stages in the humaninformation processing and the hypothetical relationshipbetween them Most of the models were consistent with thisbasic framework And on this basis Wickens et al [17] putforward the model of information processing with the atten-tion function Sun et al [18] put forward cognitive syntheticmodel which brought cognitive process into the interac-tion between human and environment system according tothe thought of parallel processing of layered informationand output by competition and coordination Taking andintegrating the advantages of the predecessorrsquos research asreference this paper put forward the cognitive model ofhuman-machine interaction interface of cabin (shown inFigure 2)

As can be seen from Figure 2 cognitive processes areplaced in the interaction system of human machine andenvironment Through visual and auditory organ the oper-ators observe the systemrsquos operating situation and proceedwith sensory processing Combining call rules and knowl-edge in long-term memory and call targets and tasks inshort-term memory with constraint module the sensory

information processing is conducted Information process-ing includes intuition layer template layer reasoning layerand comparator Information is parallel-processed by thethree layers of intuition template and reasoning and theschemes are produced in the competition and cooperationin the three layers and output in the comparator Finallythe selected corresponding method is carried out Infor-mation acquisition processing and performing all need tointeract with the constraint module which is the operatorsrsquosubjective understanding habits and principle of informa-tion processing Perception decision-making and responseimplementation all need to consume attention In order toobtain the best cognitive ergonomics appropriate cognitivestrategies should be adopted to balance the quality and speedof the information processing in cognitive systemThismodelcould describe the operatorsrsquo cognitive process clearly so itis suitable for application in the layout design of human-machine interaction interface of cabin

23 Summarize the Layout Principles Based on CognitivePsychology The information processing is not only affectedby stimulation but also influenced by the past experienceand knowledge To improve the efficiency of human-machineinteractive information cognitive law involving the human-machine interaction interface layout design should be sum-marized whichwould be transformed into guiding principlesfor layout design [19 20]

Principle 1 (cognition corresponds to objective) Using thehuman-machine interaction interface in line with usersrsquoexperience and knowledge and adopting the appropriateprocessing method conform to the old habits and conceptswhich could reduce the learning time and memory timeand avoid the happening of mistakes For instance themanipulators with similar functions should be arranged inthe same area Because when the manipulators with similarfunctions needed to be used the attention of operators would

4 Computational Intelligence and Neuroscience

Principle 1 Cognition corresponds to objective

According to the functional partition

Principle 2 Task flow design

According to the operating sequence to arrange

Principle 3 Human-machineinteraction

interface matches with the usersrsquo cognitive

strategy

According to the importance and operating frequency to arrange

Principle 4 In accordance with

information organization law

According to the correlation to arrange

Arranged in one area when coding

Fitness function

Ti

Si

Oij

Figure 3 The process of building objective function

search the manipulator at the familiar area of interface in anautomatic way

Principle 2 (task flow design) Through the operatingsequence design to reduce the usersrsquo workload and improvethe working efficiency in view of the main tasks of theoperator that need to be done the execution process oftasks should be analyzed And then according to operatorsrsquocognitive habits to merge or reduce the unnecessary actionsor implement automation so as to simplify the dialogueprocess of human-machine interaction which would speedup the information processing and reduce the usersrsquo cognitiveload and cognitive time in information processing

Principle 3 (human-machine interaction interface matcheswith the usersrsquo cognitive strategy) Human cognition haddynamic characteristic the human-machine interactioninterface was very difficult to adapt to the dynamic cognitivedifferences of all kinds of users So based on usersrsquo knowledgelevel cognitive ability and habits meanwhile by judging thedifferent levels of usersrsquo cognitive strategy human-machineinteraction interface would be designed intelligently so as toadapt to the usersrsquo cognitive process dynamically For exam-ple according to the characteristics ofmemory the importantdisplay andmanipulator whichwould be frequently observedandmanipulated should be arranged in the convenient rangefor observation and comfortable range for manipulationTheburden on the usersrsquo short-term memory could be easedAnd it was advantageous to reasonably distribute cognitiveload between layers of intuition template and reasoning andavoiding forgetting and memory errors such as consequence

Principle 4 (in accordance with information organizationlaw) The discrete stimulate in view could be organizedtogether and formed the vision of a whole by the certainrelationship between them and this phenomenonwas knownas the visual organization features Visual organizationalprinciples mainly included proximity principle similarity

principle and closeness principle which had a certain guid-ing significance in the interface design For example supposethere was lots of information that needed to be displayed onthe screen in order tomake the information displayed clearlythe relevant information should be put together by proximityprinciple or be expressed in the same color by similarityprinciple So this information seemed to be whole and theusers could detect them rapidly and accurately

24 Determine the Optimization Objective according to theLayout Principles The human-machine interaction interfacelayout problem could be transformed into the combinatorialoptimization problem that is the layout scheme whichmost met the layout principles was sought from differentpermutation and combination scheme of the layout objectsThe optimization objectivewas to find the best layout schememaking the objective function get the maximum value Theprocess of building objective function is shown in Figure 3

In short layout Principle 1 embodied in the layout objectswith similar function will be arranged in one area LayoutPrinciple 2 is embodied in the arrangement in accordancewith the physical activities characteristics from left to rightand from top to bottom Layout Principle 3 embodied in theimportant layout objects will be arranged near the best layoutpoint 119896 Layout Principle 4 embodied in the related layoutobjects will be arranged close to each other

According to the layout principles the objective functionis defined as follows

119891 (119894)best

= argmax[

[

119899

sum

119894=1

1205751sdot 119879119894+

119899

sum

119894=1

1205752sdot 119878119894+

119899

sum

119894=1

119899

sum

119895=119894+1

119874119894119895

119889119894119895

]

]

(1)

1205751= 1 minus

119894

119899 (2)

1205752= 1 minus

119889119894119896

119889119896119898

(3)

Computational Intelligence and Neuroscience 5

SelectCross

Mutate

ttat0

AAGA

N+ 1

N + 1Define the target and the fitness function

Generate a number of optimal solutions

The layout design of human-machineinteraction interface

Output the optimal solution

Update the pheromone of each route

m ants traversal n nodes after the ants pass throughaccording to the optimal route to increase the pheromone

Calculate the probability of each ant moving to the next nodethen according to probability to move the ants to the next node

Initialization parameters take the optimal solution generated by GAas the distribution of initial pheromoneput m ants on n nodes

a

Figure 4 The process of GA-ACA

where 119879119894represents the weight of layout object 119894 relative

to Principle 2 119878119894represents the weight of layout object 119894

relative to Principle 3 119874119894119895represents the correlation between

layout object 119894 and layout object 119895 relative to Principle4 119889119894119895represents the distance between layout object 119894 and

layout object 119895 119899 represents the number of layout objects 119894represents the position number of layout objects 120575

1and 120575

2

represent the position control coefficients 119889119894119896represents the

distance between the layout object 119894 and the best layout point119896 119889119896119898

represents the maximum distance between a layoutpoint and the best layout point 119896 in the whole layout range

25 Establish the Layout Design Model of Human-MachineInteraction Interface Based on GA-ACA

251 GA-ACA In 1975 according to Darwinrsquos theory ofsurvival of the fittest the American scholar John Hollandput forward the genetic algorithm [21] Through parallel andglobal search mode to search the best individual in the opti-mization group GA had good adaptive ability robustnessgenerality and othermerits widely used inmachine learningpattern recognition image processing and other fields

In the early 1990s inspired from antsrsquo foraging behaviorin the nature world the Italian scholar Dorigo et al presentedant colony algorithm [22ndash24] ACA had very strong robust-ness and ability to search a good solution and easy to parallelimplementation Originally it is used to solve the travelingsalesman problem latter widely applied to solve the classicaloptimization problem such as sequential ordering problemand quadratic assignment problem

GA and ACA were stochastic optimization algorithmboth of which had the advantages of global search andrandom search and easily combined with other algorithmsHowever GA had the shortcomings of huge calculation andpoor stability leading to low precision and efficiency ACAneeded long search time and is easily prone to stagnationphenomenonThe combination of GA and ACA could utilizethe advantage of the two algorithms and overcome theirdisadvantages and the research has proved that the hybridalgorithm had good efficiency [25] This new algorithm wasused for multiple sequence alignment [26] initializationfor synchronous sequential circuits [27] hardwaresoftwarepartitioning [28] and other optimization problems

According to the study and experiment of GA and ACA[29] the speed-time curve is shown in Figure 4 In the earlystages of the search (119905

0sim 119905119886) GA has fast convergence When

evolution reaches a certain degree its evolutionary ratewouldfall sharply namely the efficiency is low after 119905

119886 Instead at

the beginning of the search (1199050sim 119905119886) ACA searches slowly

But after the accumulation of pheromone reaches a certainextent the searching activity of ants can present a certainregularity that is to say the speed is rapidly improved after119905119886As shown in Figure 4 this paper put forward combining

the advantages of the two algorithms in layout design GAis adopted in the former process of algorithm (before point119886) and the rapidity randomness and global convergence ofGA are used A number of layout schemes of human-machineinteraction interface are generated as initial solution whichwill be turned into track intensity distribution as initializationpheromone Then ACA is adopted to optimize the layoutschemes in the latter process of algorithm (after point119886) In the case of a certain track intensity distribution ofinitialization pheromone the characteristics of parallelismpositive feedbackmechanism and high solving efficiencywillbe used to improve the efficiency of solving and output theoptimal solution

252 GA to Determine the Initial Pheromone

(1) The Operating Mechanism of GA The process of GA inhuman-machine interaction interface layout is shown at theleft part in Figure 4

(a) Coding Using the form of sequence coding the layoutobjects were expressed by real number to carry out optimiz-ing calculation The code string format shows as follows

119862119898= [119888119898

1 119888119898

2 119888119898

3 119888

119898

119899] (4)

where 119898 represents the population size 119899 represents thenumber of the layout objects 119888119898

119899represents the layout object

in the 119899th position of the119898th chromosomeSuppose there are 10 layout objects which randomly

generated an individual such as [5 4 3 8 7 2 10 1 9 6]Thecode string represents layout object 5 in the first positionlayout object 4 in the second position and so on There are

6 Computational Intelligence and Neuroscience

direct relationships between the objective function and thecoordinate of layout objects If the codes of layout objectsare in different position the corresponding coordinatesare different and the objective function value will also bedifferent

(b) Construct the Fitness Function Take each kind of permu-tation and combination way as an individual and then thebest layout solution was sought by calculating the individualrsquosfitness value and evolutionary operationThe119901th fitness valueof chromosome is as follows

fitness (119901) =119899

sum

119894=1

1205751sdot 119879119894+

119899

sum

119894=1

1205752sdot 119878119894+

119899

sum

119894=1

119899

sum

119895=119894+1

119874119894119895

119889119894119895

(5)

The three parts of fitness function reflect the comprehen-sive situation of layout Principles 2 3 and 4 respectivelythe pros and cons of layout schemes will be judged from theoverall layout

(c) Determine the Evolutionary Mechanism It is done startingfrom the random generation of initial population constantlyrepeating the process of selection crossover and mutationoperation and the population developed along the directionof established goals from generation to generation Accordingto the fitness value of the fitness function excellent indi-viduals would be selected from the current population bywheel bet method so as to form a new population Usingsequence crossover method parent individuals exchangedpart of genes and thus new individuals would be formedUsing swapmutationmethod the diversity of populationwasguaranteed and immature convergence phenomenon wasprevented

(2) The Combination of GA and ACA The combinationopportunity of GA and ACA was dynamically determinedin the operational process of GA First set minimum geneticiterations Genmin andmaximum genetic iterations Genmax inGAThen according to formula (6) record the evolution rateof progeny population in the iterative process and set theminimumevolution rateGen

119901of progeny populationWithin

the scope of the given number of iterations if successiveGen119902generations the evolution rate of progeny population

were less than Gen119901 this showed that the optimization speed

was very low The process of GA should be terminated andturned into the ACA The 10 of the optimal individuals inlast generation in GA would be selected as the carrier whichwould be taken as the suboptimal solution to distribute theinitial pheromone in ACA and optimal solution would beobtained further

EV119896 =sum119898

119894=1(119891119896

119894minus 119891119896minus1

119894) 119891119896minus1

119894

119898 119896 = 2 3 119899 (6)

where 119898 represents population size 119896 represents evolutionalgeneration 119891119896

119894represents the fitness value of individual 119894 in

119896th iteration

253 ACA to Solve Pareto Solution The purpose of layoutoptimization of human-machine interaction interface was to

get an optimal solution to satisfy the design goal which wasto find an optimal path from layout object 119894 to layout object119895 and the optimized goal was to get a maximum of (1) Inthe ACA system model each feasible solution represented apath that an ant walked by It is a shortest path problem sothe objective function of ACA was defined as the reciprocalof the objective function of GA

119871119896=

1

119891 (119894)best (7)

(1) State Transition Rules The process of ACA in human-machine interaction interface layout is shown at the right partin Figure 4 Take the layout of manipulators to describe therealization process of ACA [30 31] Set 119887

119894(119905) (119894 = 1 2 119899)

represents the number of ants of manipulator 119894 in time 119905119898 =

sum119899

119894=1119887119894(119905) represents the total number of ants and 119899 represents

the number of manipulators Set tabu119896represents the tabu

table of ant 119896 which will be adjusted dynamically along withthe ant optimization process In initial stage 119898 ants willbe placed on 119899 manipulators randomly The first elementof each antrsquos tabu table will be set as its first manipulatorIn the process of each iteration each ant chooses the nextmanipulator by the state transition rules repeatedly Afterselection for 119899minus1 times a group ofmanipulators layoutwill begenerated eventually The probability of ant 119896 transfers frommanipulator 119894 to manipulator 119895 in time 119905 is as follows

119901119896

119894119895(119905) =

[120591119894119895(119905)]120572

[120578119894119895]120573

sum119897isin119880

[120591119894119897(119905)]120572

[120578119894119897]120573

119895 isin 119880

0 119895 notin 119880

120578119894119895=

1

V119894119895

V119894119895=10038161003816100381610038161003816119879119894minus 119879119895

10038161003816100381610038161003816+10038161003816100381610038161003816119878119894minus 119878119895

10038161003816100381610038161003816+10038161003816100381610038161003816119874119894minus 119874119895

10038161003816100381610038161003816

(8)

where 120591119894119895(119905) represents the pheromone between manipulator

119894 and manipulator 119895 at 119905 moment 120578119894119895represents the visibility

(heuristic information) of transferring from manipulator 119894to manipulator 119895 V

119894119895represents the difference value of com-

prehensive weight between manipulator 119894 and manipulator119895 to the layout principles 120572 (120572 ge 0) represents the relativeimportance of 120591 120573 (120573 ge 0) represents the relative importanceof 120578 119880 represents the feasible point set namely the set ofmanipulators can be chosen by ant 119896 at 119905moment

(2) Pheromone andHeuristic InformationThis paper adoptedthe max-min ant system (MMAS) which was proposed bythe Belgium scholar Thomas Due to the combined withGA initialized pheromone was different fromMMAS At theinitial moment initial pheromone value is set as follows

120591119894119895(0) = 120591

119862+ 120591119866 (9)

where 120591119862is the given pheromone constant based on the

specific scale of solution equivalent to the 120591min in MMAS120591119866represents the value of pheromone converted from the

solution of GA

Computational Intelligence and Neuroscience 7

Table 1 Partition coding for manipulators

The name of the manipulator Code The name of the manipulator Code

Range 1

E-BRAKE 1

Range 3

LEFT CATHEAD 9GEN E-STOP 2 RIGHT CATHEAD 10VFD E-STOP 3 ROTARY CATHEAD 11

PARKING BRAKE 4 ALARM 12

Range 2

RT MOTOR CONTROL 5

Range 4

AIR SPINNER 13RT INERTIA BRAKE 6 TOOLS SELECT 14RT SPEED SETTING 7 SPARE1 15RT TORQUE LIMIT 8 SPARE2 16

Ant chose next manipulator mainly according topheromone 120591

119894119895(119905) and heuristic information 120578

119894119895 120578119894119895

wasgiven by the layout problem to be solved and affected by V

119894119895

which remained unchanged during the operation processin algorithm Ants released pheromone on the path theypassed by In order to avoid too much residual informationto submerge heuristic information residual informationshould be upgraded After ants traversed through all themanipulators the pheromone updated in the environmentThe update equation is as follows

120591119894119895(119905 + 1) = (1 minus 120588) sdot 120591

119894119895(119905) + Δ120591

119894119895(119905)

Δ120591119894119895(119905) = sumΔ120591

119896

119894119895(119905)

Δ120591119896

119894119895(119905) =

119876

119871119896

(ant 119896 passes though (119894 119895))

0 (otherwise)

(10)

where 120588 (0 le 120588 le 1) represents the volatilization coefficientof pheromone Δ120591119896

119894119895(119905) represents the amount of pheromone

released between manipulator 119894 and manipulator 119895 by ant 119896in this cycle Δ120591

119894119895(119905) represents the increment of pheromone

between manipulator 119894 and manipulator 119895 after this cycle 119876is a constant which represents the amount of pheromone 119871

119896

represents the objective function value corresponding withthe layout of all the manipulators formed by ant 119896 traversedthrough in this cycle

Only the ant with optimal layout scheme could updatepheromone in one cycle The amount of pheromone on eachpath would be limited in scope of [120591min 120591max] If beyondthis range the pheromone would be mandatory set as 120591minor 120591max Compared with the standard ACA the improvedalgorithm prevented premature stagnation With all theants completing the traverse of all the manipulators thepheromone accumulated and volatilized continuously Untilreaching the number of iterations or a certain fitness valuethe optimal path formed by ants was the best layout scheme

3 Application Verification

31 Layout Design of Driller Control Room on Drilling RigTake the layout design of human-machine interaction inter-face of driller control room on drilling rig as an exampleto illustrate the proposed method The 16 manipulators of

Table 2 The size of each type of manipulators (mm)

The types of manipulators Size The codes of manipulatorsSwitch knobs Φ30 4 5 6 9 10 11 12 13 14 15 16Rotary knobs Φ50 7 8Buttons Φ40 1 2 3

ZJ1209000DB rig console would be arranged (the grey areaas shown in Figure 5) First of all the 16 manipulators needto be encoded As shown in Table 1 when encoding layoutPrinciple 1 needs to be reflected that is manipulators withsimilar function will be arranged in one area to reduce thesearch time for operators

It is necessary to simplify the layout area and layoutobjects when describing themathematical model to facilitatea digital description of design variables According to thehuman upper limb dimension to determine the layout spaceand simplify the layout area as rectangular area (450 times

400mm) there are three types of manipulators includingbuttons rotary knobs and switch knobs Their sizes areslightly different and the specific sizes are shown in Table 2The spaces between manipulators are set as equal and theinterval is 100mm After being simplified the layout ofmanipulators can be regarded as a scheduling problem firstusing GA-ACA to find a good sorting and then according tothe order to establish the actual layout

Drilling process is mainly divided into three workingconditions pull out of hole run in hole and normal drillingConsidering the principle of operating sequence in multipleworking conditions determine the manipulatorsrsquo operatingsequence and calculate each manipulatorrsquos weight relativeto Principle 2 Through questionnaires to consult drillersdetermine the relative judgment matrix of the manipulatorsrsquoimportance and frequency and calculate each manipulatorrsquosweight relative to Principle 3 Analytic hierarchy process willbe used to calculate each manipulatorrsquos weight of 119879

119894and

119878119894 and the calculation result is shown in Table 3 Finally

determine the relative correlation between the manipulatorsThe correlation between manipulator and itself is expressedwith 1 and the correlation between themanipulator and othermanipulators is expressed in decimal within 0sim1 The greaterthe correlation between manipulators the greater the corre-lation value The correlation between the 16 manipulators isshown in Table 4

8 Computational Intelligence and Neuroscience

Table 3 The manipulatorsrsquo weight relative to Principles 2 and 3

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16119879119894

0018 0018 0018 0055 0113 0076 0205 0163 0068 0068 0068 0016 0016 0098 0 0119878119894

0163 0115 0115 0115 0069 0069 0069 0069 0043 0043 0043 0013 0026 0026 0011 0011

LEFT DRUMSHIFT LOCK

AIR SLIPS

LEFT DRUMCLUTCH

AIR HORN

AD MODE AD MOTOR

LEFT AD LEFT ADCLUTCH

TOP SCREENWIPER CONTROL SPEED SETTING

PROTECTION RESET

SILIENCERESET

DW SPEEDCONTROL

KEYBOARD12 13 3 2 9

101 4

5 7

6 8

11 14

15 16

PARKINGBRAKE

RT INERTIABRAKE

RT SPEEDSETTING

TOOLSSELECT

E-BRAKE

VFDE-STOP E-STOPGEN

RT MOTORCONTROL

RT TORQUELIMIT

LEFT

RIGHTCATHEAD

CATHEAD

ROTARYCATHEAD

ALARM

AIRSPINNER

SPARE2SPARE1

Armrest

WORKINGBRAKERIGHT DRUM

SHIFT LOCKRIGHT DRUMCLUTCH

RIGHT ADSHIFT LOCH

SHIFT LOCH

RIGHT ADCLUTCH

FRONT SCREENWIPER CONTROL

Number 2 PUMP

Number 1 PUMP Number 3 PUMP

SPEED SETTING

SPEED SETTING

Figure 5 Layout schematic diagram of ZJ1209000DB rig console (before optimization)

32 Optimization Result After completing the above datapreparation as shown in Figure 6 the operating parametersof GA and ACA need to be set up before the computeraided optimization calculation proceeds First set the controlparameters of GA population size 119873 = 50 crossover rate119901119888= 06 mutation rate 119901

119898= 02 and number of partition

119904 = 4 Second set the control parameters ofACAThe amountof ants is119898 = 10 120572 = 1 120573 = 1 and 120588 = 05

In the system of GA-ACA calculation module set theend condition of GA minimum genetic iterations Genmin =

15 maximum genetic iterations Genmax = 50 minimumevolution rate Gene

119901= 3 and Gene

119902= 3 Set the control

parameters of pheromone 119876 = 500 120591min = 10 and 120591max =

100 The optimal 10 of individuals in the last generationin GA would be selected as the solution set of geneticoptimization which would be transformed into pheromone

values If manipulator 119894 was adjacent to manipulator 119895 ingenetic optimization solution the pheromone added 10 onthe path (119894 119895) and all the initial values of pheromonesshould be set like this When meeting one of the followingconditions theACA should stop (1)Thenumber of iterationsreaches antmax = 50 (2) For continuous three generations inthe iteration the improvement rate of offspring optimizationis less than 05

Click the button of algorithm running on the systeminterface the system calculates via the program after 50times genetic iterations and 11 times ant colony optimizationiteration the value of the optimal layout scheme is 2822The sequence of the manipulators is obtained that is 4 13 2 6 5 7 8 9 10 12 11 14 13 15 and 16 Accordingto the optimized sequence the layout scheme is shown inFigure 7

Computational Intelligence and Neuroscience 9

Table 4 The correlation between the 16 manipulators

119874119894119895

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 161 1 05 07 09 0 0 0 0 0 0 0 0 0 0 0 02 05 1 09 05 0 0 0 0 0 0 0 0 0 0 0 03 07 09 1 07 0 0 0 0 0 0 0 0 0 0 0 04 09 05 07 1 0 0 0 0 0 0 0 0 0 0 0 05 0 0 0 0 1 09 07 09 0 0 0 0 0 0 0 06 0 0 0 0 09 1 05 07 0 0 0 0 0 0 0 07 0 0 0 0 07 05 1 09 0 0 0 0 0 0 0 08 0 0 0 0 09 07 09 1 0 0 0 0 0 0 0 09 0 0 0 0 0 0 0 0 1 09 07 05 0 0 0 010 0 0 0 0 0 0 0 0 09 1 07 05 0 0 0 011 0 0 0 0 0 0 0 0 07 07 1 05 0 0 0 012 0 0 0 0 0 0 0 0 05 05 05 1 0 0 0 013 0 0 0 0 0 0 0 0 0 0 0 0 1 09 03 0314 0 0 0 0 0 0 0 0 0 0 0 0 09 1 03 0315 0 0 0 0 0 0 0 0 0 0 0 0 03 03 1 0916 0 0 0 0 0 0 0 0 0 0 0 0 03 03 09 1

Table 5 The comparison between GA ACA and ACA

Algorithm Layout scheme The value of layout scheme Number of iterationsGA 4 2 3 1 7 8 6 5 12 11 9 10 13 14 15 16 2814 82ACA 4 1 2 3 5 6 7 8 9 10 11 12 15 16 14 13 2773 75GA-ACA 4 1 2 3 7 8 5 6 12 9 10 11 15 16 13 14 2835 50 + 13

Figure 6 System interface of layout design

Because the proposed method is still in the research anddevelopment there is certain difference between the solutionand the actual situation Besides the human cognitive activ-ities and layout experience cannot be completely describedby the layout model layout scheme obtained by algorithmoptimization is close to the optimal solution rather than theoptimal solution In this stage the designer needs to adjustthe layout scheme according to the actual needs Similarlysort the manipulators on the console at the left hand Forthe sake of evaluating layout scheme intuitively computersimulation design modeling is proceeded Through detaileddesign the final layout scheme is shown in Figure 8

The CATIA software will be used to evaluate the resultof the layout CATIA V5 integrates four ergonomic moduleswhich can evaluate visibility accessibility the comfort ofposture (analyze the angle of each joint) and so on The bluearea in Figure 9 shows the reach envelope of drillerrsquos righthand Under natural state the drillerrsquos arm can operate themain manipulators on console This shows that the locationsof manipulators were in the human bodyrsquos correspondingrange of joint motion and in accord with the armrsquos motiontrail In Figure 10 the virtual human model simulates oneoperating posture of driller And the postural score analysisin Figure 11 shows the comfort score of lumbar vertebrathoracic vertebra head arm and forearmThe sores indicatethat this operating posture is comfortable the position of thismanipulator matches with the movement characteristics ofthe human body

33 Algorithm Comparison Using single GA ACA and GA-ACA hybrid algorithm to solve the above layout optimizationproblem respectively set the number of test times as 10each Result comparison is shown in Table 5 the columnof layout scheme shows the optimal result in 10 timesrsquorunning and the third and the fourth columns show theaverage value of layout scheme and the average number ofiterations As can be seen from the test result no matterthe solution speed or precision the GA-ACA is superiorto single GA and ACA in solving layout optimizationproblem

10 Computational Intelligence and Neuroscience

GEN E-STOPVFDE-STOP

LEFT RIGHT

E-BRAKEPARKINGBRAKE

RT INERTIABRAKE

RT SPEEDSETTING LIMIT

RT MOTORCONTROL

RT TORQUE ROTARYCATHEAD

CATHEAD CATHEAD

AIR SPARE1 SPARE2

ALARM

TOOLSSELECT

WORKINGBRAKE

Armrest0

200

0 200 400 600 800 10002004006008001000

400

600

800

Comfortableoperating range

operating rangeMaximum

1 3 24

56

7 8

9 10

1112

1314 15 16

SPINNER

Range 1

Range 2 Range 3

Range 4

y

x

Figure 7 Layout scheme of right console (after optimization)

Figure 8 Modeling and simulation of layout scheme

Figure 9 Analyze the right handrsquos reach envelope

34 Discussion Through reference to previous researchabout cognitive model this paper integrated their advantagesand put forward the cognitive model of human-machineinteraction interface suitable for cabin This model describedthe cognitive process of operators working in the cabinAnd then by analyzing the information processing processthe layout principles of human-machine interaction interfacewere summed up which would be quantified in the objectivefunction However human cognitive behavior process is verycomplicated part of the cognitive activities is difficult toexpress by formalized methods layout principles needed tobe improved further There were lots of influence factors in

Figure 10 The simulation of operating posture

Figure 11 The comfort evaluation of operating posture

actual engineering problem the layout problem was difficultto be described completely by mathematical model

It has become common that the bionic intelligencealgorithms were applied in the layout optimization designbut cognitive characteristics were seldom considered as theconstraints of layout optimization algorithm FurthermoreGA-ACA combined the advantages of two algorithms whichwould be more precise to solve layout optimization problemThe layout design of human-machine interaction interfaceof driller control room on drilling rig has illustrated theproposed method According to the characteristics of thehuman-machine interaction interface of different product

Computational Intelligence and Neuroscience 11

this method could be modified and adapted As a layoutergonomic design method it could assist designers andengineers to conduct human-machine interaction interfacedesign and improve the efficiency in layout design

4 Conclusion

Based on the theory of cognitive psychology according toWickens information processing model the cognitive modelof human-machine interaction interface was establishedConsidering peoplersquos information organization law visualsearch law user memory characteristics and so on thehuman-machine interaction interface should conform tooperatorsrsquo cognitive ability for interface information Basedon the cognitive model the layout principles of human-machine interaction interface were summarized as the layoutconstraintsThe problem of solving layout scheme was trans-formed into combinatorial optimization problem and GA-ACA was put forward to solve the problem which realizedthe algorithmization of artificial optimization process

Taking the layout of manipulators as example the objec-tive function of layout optimization was built according toeach principle Use GA to generate the solution of layoutscheme which would be transformed into initial pheromoneas ACA required Taking the difference value of comprehen-sive weight of the manipulators for the layout principles asheuristic information ofACA combinedwith the pheromoneprovided by GA the positive feedback optimization mecha-nism of ACA was used to solve further GA-ACA integratedthe complementary advantages of GA and ACA which hadgood optimizing performance and time performance andimproved the design efficiency Taking the 16 manipulators ofZJ1209000DB rig console as layout example layout designsystem of human-computer interaction interface for cabinwas developed by Visual Basic which validated the abovelayout optimization method

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgments

This research was supported by Open Fund (OGE201403-23) of Key Laboratory of Oil amp Gas Equipment Ministryof Education (Southwest Petroleum University) and by theOpen Research Subject (GY-14YB-31) of Key Laboratory(Research Base) of Industrial Design Industry Research Cen-ter Humanities and Social Science EducationDepartment ofSichuan Province

References

[1] J Z Cha X J Tang and Y P Lu ldquoSurvey on packing problemsrdquoJournal of Computer-AidedDesignampComputer Graphics vol 14pp 705ndash711 2002

[2] K A Dowsland and W B Dowsland ldquoPacking problemsrdquoEuropean Journal of Operational Research vol 56 no 1 pp 2ndash14 1992

[3] S Margaritis and NMarmaras ldquoSupporting the design of officelayout meeting ergonomics requirementsrdquo Applied Ergonomicsvol 38 no 6 pp 781ndash790 2007

[4] N Shariatzadeh G Sivard and D Chen ldquoSoftware evalu-ation criteria for rapid factory layout planning design andsimulationrdquo in Proceedings of the 45th CIRP Conference onManufacturing Systems pp 299ndash304 Athens GreeceMay 2012

[5] J Z Huo G Q Li H F Teng and Z G Sun ldquoHuman-computercooperative ant colonygenetic algorithm for satellite modulelayout designrdquo Chinese Journal of Mechanical Engineering vol41 no 3 pp 112ndash116 2005

[6] R Li D M Zhuang R Wang and L R Wang ldquoOptimizationabout the layout work of the steering arrangement in thecockpitrdquo Journal of System Simulation vol 16 pp 1305ndash13072004

[7] S Y Yan Y Chen and L Y Liang ldquoOptimization of controlslayout based on simulated annealing algorithmrdquo Nuclear PowerEngineering vol 35 no 1 pp 67ndash70 2014

[8] L C Zong C Ye S H Yu and D K Chen ldquoResearchand application of intelligent layout method in DSV cabinequipmentrdquo Shipbuilding of China vol 54 no 3 pp 147ndash1542013

[9] L C Zong S H Yu J B Sun L W Han and S S An ldquoStudyon cabin layout optimization with fish algorithmrdquo MechanicalScience and Technology for Aerospace Engineering vol 33 pp257ndash262 2014

[10] Y L Wang C Wang Z S Ji and X G Zhao ldquoA study onintelligent layout design of ship cabinrdquo Shipbuilding of Chinavol 54 pp 139ndash146 2013

[11] D HWolpert andWGMacready ldquoNo free lunch theorems foroptimizationrdquo IEEE Transactions on Evolutionary Computationvol 1 no 1 pp 67ndash82 1997

[12] F Q Zhao G Q Li C Yang A Abraham and H B LiuldquoA human-computer cooperative particle swarm optimizationbased immune algorithm for layout designrdquo Neurocomputingvol 132 pp 68ndash78 2014

[13] A Lodi S Martello and M Monaci ldquoTwo-dimensionalpacking problems a surveyrdquo European Journal of OperationalResearch vol 141 no 2 pp 241ndash252 2002

[14] J R AndersonCognitive Psychology and Itrsquos Implications editedby Y L Qin Posts amp Telecom Press 2012

[15] A Ghanbari S M R Kazemi F Mehmanpazir and M MNakhostin ldquoA cooperative ant colony optimization-geneticalgorithm approach for construction of energy demand fore-casting knowledge-based expert systemsrdquo Knowledge-BasedSystems vol 39 pp 194ndash206 2013

[16] J B Best Cognitive Psychology X T Huang Ed China LightIndustry Press Beijing China 2000

[17] C Wickens J G Hollands S Banbury and R ParasuramanEngineering Psychology amp Human Performance Pearson Lon-don UK 4th edition 2012

[18] L Y Sun Z X Li and T S Jin ldquoCognitive synthetic model andits application in HCIrdquo Journal of Xirsquoan Jiaotong University vol31 pp 74ndash80 1997

[19] Y P Zhang ldquoHuman-computer interface design based onknowledge of cognitive psychologyrdquo Computer Engineering andApplications vol 30 pp 105ndash107 2005

12 Computational Intelligence and Neuroscience

[20] Z Lan ldquoThe cognitive basis of software interface designrdquo ShanxiScience and Technology no 3 pp 41ndash42 1998

[21] X P Wang and L M Cao Genetic AlgorithmmdashTheory Appli-cation and Software Implementation Xirsquoan Jiaotong UniversityPress Xirsquoan China 2002

[22] E Bonabeau M Dorigo and G Theraulaz ldquoInspiration foroptimization from social insect behaviourrdquoNature vol 406 no6791 pp 39ndash42 2000

[23] M Dorigo and L M Gambardella ldquoAnt colonies for thetravelling salesman problemrdquo BioSystems vol 43 no 2 pp 73ndash81 1997

[24] MDorigo VManiezzo andA Colorni ldquoAnt system optimiza-tion by a colony of cooperating agentsrdquo IEEE Transactions onSystems Man and Cybernetics Part B Cybernetics vol 26 no1 pp 29ndash41 1996

[25] J-L Ding Z-Q Chen and Z-Z Yuan ldquoOn the Markovconvergence analysis for the combination of genetic algorithmand ant algorithmrdquo Acta Automatica Sinica vol 30 no 4 pp629ndash634 2004

[26] Z-J Lee S-F Su C-C Chuang and K-H Liu ldquoGeneticalgorithmwith ant colony optimization (GA-ACO) formultiplesequence alignmentrdquo Applied Soft Computing vol 8 no 1 pp55ndash78 2008

[27] Z Li C P Xu W Mo and G J Chen ldquoInitialization forsynchronous sequential circuits based on ant algorithm ampgenetic algorithmrdquo Acta Electronica Sinica vol 31 pp 1276ndash1280 2003

[28] Z-H Xiong S-K Li and J-H Chen ldquoHardwaresoftware par-titioning based on dynamic combination of genetic algorithmand ant algorithmrdquo Journal of Software vol 16 no 4 pp 503ndash512 2005

[29] J-L Ding Z-Q Chen and Z-Z Yuan ldquoOn the combinationof genetic algorithm and ant algorithmrdquo Journal of ComputerResearch and Development vol 40 no 9 pp 1351ndash1356 2003

[30] T Stutzle and H H Hoos ldquoMAX-MIN ant systemrdquo FutureGeneration Computer Systems vol 16 no 8 pp 889ndash914 2000

[31] T Stuetzle and H Hoos ldquoMAX-MIN Ant System and localsearch for the traveling salesman problemrdquo in Proceedings ofthe IEEE International Conference on Evolutionary Computation(ICEC rsquo97) pp 309ndash314 Indianapolis Indiana April 1997

Submit your manuscripts athttpwwwhindawicom

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Applied Computational Intelligence and Soft Computing

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Electrical and Computer Engineering

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ArtificialNeural Systems

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RoboticsJournal of

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Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Computational Intelligence and Neuroscience

Industrial EngineeringJournal of

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Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Human-ComputerInteraction

Advances in

Computer EngineeringAdvances in

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Computational Intelligence and Neuroscience 3

Machine Rig

DisplayInput

Stimulate

Receptor

Short-term sensory storage

Perception

Response implementation

Long-term memory

OutputManipulator

Decision-making

information

Attention

Working memory

HumanDriller

Intuitionlayer

Template layer

The constraint module including the operatorsrsquo subjective understanding habits and principle of information processing

Environment

Reasoning layer

Comparator

Visualhearing

and so forth

Perceptual activities

The driller control room

The human-machine interaction interface

Feedback

Figure 2 The cognitive model of human-machine interaction interface of cabin

characteristics about mental labor in cognitive psychol-ogy such as the research of memory understanding andcommunication the designed human-machine interactioninterface tries to reduce peoplersquos cognitive burden as much aspossible making the product easy to learn easy to use andwith high efficiency The idea of human-machine interactioninterface layout design was to build cognitive model basedon the information processing mechanism Then on thebasis of peoplersquos thinking characteristics using the rule ofinformation organization visual search pattern andmemorycharacteristic the human-machine interaction interface lay-outwould conform to operatorsrsquo cognitive ability for interfaceinformation

There were plenty of studies on cognitive processespsychologistsrsquo emphasis on the study of cognitive processeswas different at different periods Information processingmodel described the main elements or stages in the humaninformation processing and the hypothetical relationshipbetween them Most of the models were consistent with thisbasic framework And on this basis Wickens et al [17] putforward the model of information processing with the atten-tion function Sun et al [18] put forward cognitive syntheticmodel which brought cognitive process into the interac-tion between human and environment system according tothe thought of parallel processing of layered informationand output by competition and coordination Taking andintegrating the advantages of the predecessorrsquos research asreference this paper put forward the cognitive model ofhuman-machine interaction interface of cabin (shown inFigure 2)

As can be seen from Figure 2 cognitive processes areplaced in the interaction system of human machine andenvironment Through visual and auditory organ the oper-ators observe the systemrsquos operating situation and proceedwith sensory processing Combining call rules and knowl-edge in long-term memory and call targets and tasks inshort-term memory with constraint module the sensory

information processing is conducted Information process-ing includes intuition layer template layer reasoning layerand comparator Information is parallel-processed by thethree layers of intuition template and reasoning and theschemes are produced in the competition and cooperationin the three layers and output in the comparator Finallythe selected corresponding method is carried out Infor-mation acquisition processing and performing all need tointeract with the constraint module which is the operatorsrsquosubjective understanding habits and principle of informa-tion processing Perception decision-making and responseimplementation all need to consume attention In order toobtain the best cognitive ergonomics appropriate cognitivestrategies should be adopted to balance the quality and speedof the information processing in cognitive systemThismodelcould describe the operatorsrsquo cognitive process clearly so itis suitable for application in the layout design of human-machine interaction interface of cabin

23 Summarize the Layout Principles Based on CognitivePsychology The information processing is not only affectedby stimulation but also influenced by the past experienceand knowledge To improve the efficiency of human-machineinteractive information cognitive law involving the human-machine interaction interface layout design should be sum-marized whichwould be transformed into guiding principlesfor layout design [19 20]

Principle 1 (cognition corresponds to objective) Using thehuman-machine interaction interface in line with usersrsquoexperience and knowledge and adopting the appropriateprocessing method conform to the old habits and conceptswhich could reduce the learning time and memory timeand avoid the happening of mistakes For instance themanipulators with similar functions should be arranged inthe same area Because when the manipulators with similarfunctions needed to be used the attention of operators would

4 Computational Intelligence and Neuroscience

Principle 1 Cognition corresponds to objective

According to the functional partition

Principle 2 Task flow design

According to the operating sequence to arrange

Principle 3 Human-machineinteraction

interface matches with the usersrsquo cognitive

strategy

According to the importance and operating frequency to arrange

Principle 4 In accordance with

information organization law

According to the correlation to arrange

Arranged in one area when coding

Fitness function

Ti

Si

Oij

Figure 3 The process of building objective function

search the manipulator at the familiar area of interface in anautomatic way

Principle 2 (task flow design) Through the operatingsequence design to reduce the usersrsquo workload and improvethe working efficiency in view of the main tasks of theoperator that need to be done the execution process oftasks should be analyzed And then according to operatorsrsquocognitive habits to merge or reduce the unnecessary actionsor implement automation so as to simplify the dialogueprocess of human-machine interaction which would speedup the information processing and reduce the usersrsquo cognitiveload and cognitive time in information processing

Principle 3 (human-machine interaction interface matcheswith the usersrsquo cognitive strategy) Human cognition haddynamic characteristic the human-machine interactioninterface was very difficult to adapt to the dynamic cognitivedifferences of all kinds of users So based on usersrsquo knowledgelevel cognitive ability and habits meanwhile by judging thedifferent levels of usersrsquo cognitive strategy human-machineinteraction interface would be designed intelligently so as toadapt to the usersrsquo cognitive process dynamically For exam-ple according to the characteristics ofmemory the importantdisplay andmanipulator whichwould be frequently observedandmanipulated should be arranged in the convenient rangefor observation and comfortable range for manipulationTheburden on the usersrsquo short-term memory could be easedAnd it was advantageous to reasonably distribute cognitiveload between layers of intuition template and reasoning andavoiding forgetting and memory errors such as consequence

Principle 4 (in accordance with information organizationlaw) The discrete stimulate in view could be organizedtogether and formed the vision of a whole by the certainrelationship between them and this phenomenonwas knownas the visual organization features Visual organizationalprinciples mainly included proximity principle similarity

principle and closeness principle which had a certain guid-ing significance in the interface design For example supposethere was lots of information that needed to be displayed onthe screen in order tomake the information displayed clearlythe relevant information should be put together by proximityprinciple or be expressed in the same color by similarityprinciple So this information seemed to be whole and theusers could detect them rapidly and accurately

24 Determine the Optimization Objective according to theLayout Principles The human-machine interaction interfacelayout problem could be transformed into the combinatorialoptimization problem that is the layout scheme whichmost met the layout principles was sought from differentpermutation and combination scheme of the layout objectsThe optimization objectivewas to find the best layout schememaking the objective function get the maximum value Theprocess of building objective function is shown in Figure 3

In short layout Principle 1 embodied in the layout objectswith similar function will be arranged in one area LayoutPrinciple 2 is embodied in the arrangement in accordancewith the physical activities characteristics from left to rightand from top to bottom Layout Principle 3 embodied in theimportant layout objects will be arranged near the best layoutpoint 119896 Layout Principle 4 embodied in the related layoutobjects will be arranged close to each other

According to the layout principles the objective functionis defined as follows

119891 (119894)best

= argmax[

[

119899

sum

119894=1

1205751sdot 119879119894+

119899

sum

119894=1

1205752sdot 119878119894+

119899

sum

119894=1

119899

sum

119895=119894+1

119874119894119895

119889119894119895

]

]

(1)

1205751= 1 minus

119894

119899 (2)

1205752= 1 minus

119889119894119896

119889119896119898

(3)

Computational Intelligence and Neuroscience 5

SelectCross

Mutate

ttat0

AAGA

N+ 1

N + 1Define the target and the fitness function

Generate a number of optimal solutions

The layout design of human-machineinteraction interface

Output the optimal solution

Update the pheromone of each route

m ants traversal n nodes after the ants pass throughaccording to the optimal route to increase the pheromone

Calculate the probability of each ant moving to the next nodethen according to probability to move the ants to the next node

Initialization parameters take the optimal solution generated by GAas the distribution of initial pheromoneput m ants on n nodes

a

Figure 4 The process of GA-ACA

where 119879119894represents the weight of layout object 119894 relative

to Principle 2 119878119894represents the weight of layout object 119894

relative to Principle 3 119874119894119895represents the correlation between

layout object 119894 and layout object 119895 relative to Principle4 119889119894119895represents the distance between layout object 119894 and

layout object 119895 119899 represents the number of layout objects 119894represents the position number of layout objects 120575

1and 120575

2

represent the position control coefficients 119889119894119896represents the

distance between the layout object 119894 and the best layout point119896 119889119896119898

represents the maximum distance between a layoutpoint and the best layout point 119896 in the whole layout range

25 Establish the Layout Design Model of Human-MachineInteraction Interface Based on GA-ACA

251 GA-ACA In 1975 according to Darwinrsquos theory ofsurvival of the fittest the American scholar John Hollandput forward the genetic algorithm [21] Through parallel andglobal search mode to search the best individual in the opti-mization group GA had good adaptive ability robustnessgenerality and othermerits widely used inmachine learningpattern recognition image processing and other fields

In the early 1990s inspired from antsrsquo foraging behaviorin the nature world the Italian scholar Dorigo et al presentedant colony algorithm [22ndash24] ACA had very strong robust-ness and ability to search a good solution and easy to parallelimplementation Originally it is used to solve the travelingsalesman problem latter widely applied to solve the classicaloptimization problem such as sequential ordering problemand quadratic assignment problem

GA and ACA were stochastic optimization algorithmboth of which had the advantages of global search andrandom search and easily combined with other algorithmsHowever GA had the shortcomings of huge calculation andpoor stability leading to low precision and efficiency ACAneeded long search time and is easily prone to stagnationphenomenonThe combination of GA and ACA could utilizethe advantage of the two algorithms and overcome theirdisadvantages and the research has proved that the hybridalgorithm had good efficiency [25] This new algorithm wasused for multiple sequence alignment [26] initializationfor synchronous sequential circuits [27] hardwaresoftwarepartitioning [28] and other optimization problems

According to the study and experiment of GA and ACA[29] the speed-time curve is shown in Figure 4 In the earlystages of the search (119905

0sim 119905119886) GA has fast convergence When

evolution reaches a certain degree its evolutionary ratewouldfall sharply namely the efficiency is low after 119905

119886 Instead at

the beginning of the search (1199050sim 119905119886) ACA searches slowly

But after the accumulation of pheromone reaches a certainextent the searching activity of ants can present a certainregularity that is to say the speed is rapidly improved after119905119886As shown in Figure 4 this paper put forward combining

the advantages of the two algorithms in layout design GAis adopted in the former process of algorithm (before point119886) and the rapidity randomness and global convergence ofGA are used A number of layout schemes of human-machineinteraction interface are generated as initial solution whichwill be turned into track intensity distribution as initializationpheromone Then ACA is adopted to optimize the layoutschemes in the latter process of algorithm (after point119886) In the case of a certain track intensity distribution ofinitialization pheromone the characteristics of parallelismpositive feedbackmechanism and high solving efficiencywillbe used to improve the efficiency of solving and output theoptimal solution

252 GA to Determine the Initial Pheromone

(1) The Operating Mechanism of GA The process of GA inhuman-machine interaction interface layout is shown at theleft part in Figure 4

(a) Coding Using the form of sequence coding the layoutobjects were expressed by real number to carry out optimiz-ing calculation The code string format shows as follows

119862119898= [119888119898

1 119888119898

2 119888119898

3 119888

119898

119899] (4)

where 119898 represents the population size 119899 represents thenumber of the layout objects 119888119898

119899represents the layout object

in the 119899th position of the119898th chromosomeSuppose there are 10 layout objects which randomly

generated an individual such as [5 4 3 8 7 2 10 1 9 6]Thecode string represents layout object 5 in the first positionlayout object 4 in the second position and so on There are

6 Computational Intelligence and Neuroscience

direct relationships between the objective function and thecoordinate of layout objects If the codes of layout objectsare in different position the corresponding coordinatesare different and the objective function value will also bedifferent

(b) Construct the Fitness Function Take each kind of permu-tation and combination way as an individual and then thebest layout solution was sought by calculating the individualrsquosfitness value and evolutionary operationThe119901th fitness valueof chromosome is as follows

fitness (119901) =119899

sum

119894=1

1205751sdot 119879119894+

119899

sum

119894=1

1205752sdot 119878119894+

119899

sum

119894=1

119899

sum

119895=119894+1

119874119894119895

119889119894119895

(5)

The three parts of fitness function reflect the comprehen-sive situation of layout Principles 2 3 and 4 respectivelythe pros and cons of layout schemes will be judged from theoverall layout

(c) Determine the Evolutionary Mechanism It is done startingfrom the random generation of initial population constantlyrepeating the process of selection crossover and mutationoperation and the population developed along the directionof established goals from generation to generation Accordingto the fitness value of the fitness function excellent indi-viduals would be selected from the current population bywheel bet method so as to form a new population Usingsequence crossover method parent individuals exchangedpart of genes and thus new individuals would be formedUsing swapmutationmethod the diversity of populationwasguaranteed and immature convergence phenomenon wasprevented

(2) The Combination of GA and ACA The combinationopportunity of GA and ACA was dynamically determinedin the operational process of GA First set minimum geneticiterations Genmin andmaximum genetic iterations Genmax inGAThen according to formula (6) record the evolution rateof progeny population in the iterative process and set theminimumevolution rateGen

119901of progeny populationWithin

the scope of the given number of iterations if successiveGen119902generations the evolution rate of progeny population

were less than Gen119901 this showed that the optimization speed

was very low The process of GA should be terminated andturned into the ACA The 10 of the optimal individuals inlast generation in GA would be selected as the carrier whichwould be taken as the suboptimal solution to distribute theinitial pheromone in ACA and optimal solution would beobtained further

EV119896 =sum119898

119894=1(119891119896

119894minus 119891119896minus1

119894) 119891119896minus1

119894

119898 119896 = 2 3 119899 (6)

where 119898 represents population size 119896 represents evolutionalgeneration 119891119896

119894represents the fitness value of individual 119894 in

119896th iteration

253 ACA to Solve Pareto Solution The purpose of layoutoptimization of human-machine interaction interface was to

get an optimal solution to satisfy the design goal which wasto find an optimal path from layout object 119894 to layout object119895 and the optimized goal was to get a maximum of (1) Inthe ACA system model each feasible solution represented apath that an ant walked by It is a shortest path problem sothe objective function of ACA was defined as the reciprocalof the objective function of GA

119871119896=

1

119891 (119894)best (7)

(1) State Transition Rules The process of ACA in human-machine interaction interface layout is shown at the right partin Figure 4 Take the layout of manipulators to describe therealization process of ACA [30 31] Set 119887

119894(119905) (119894 = 1 2 119899)

represents the number of ants of manipulator 119894 in time 119905119898 =

sum119899

119894=1119887119894(119905) represents the total number of ants and 119899 represents

the number of manipulators Set tabu119896represents the tabu

table of ant 119896 which will be adjusted dynamically along withthe ant optimization process In initial stage 119898 ants willbe placed on 119899 manipulators randomly The first elementof each antrsquos tabu table will be set as its first manipulatorIn the process of each iteration each ant chooses the nextmanipulator by the state transition rules repeatedly Afterselection for 119899minus1 times a group ofmanipulators layoutwill begenerated eventually The probability of ant 119896 transfers frommanipulator 119894 to manipulator 119895 in time 119905 is as follows

119901119896

119894119895(119905) =

[120591119894119895(119905)]120572

[120578119894119895]120573

sum119897isin119880

[120591119894119897(119905)]120572

[120578119894119897]120573

119895 isin 119880

0 119895 notin 119880

120578119894119895=

1

V119894119895

V119894119895=10038161003816100381610038161003816119879119894minus 119879119895

10038161003816100381610038161003816+10038161003816100381610038161003816119878119894minus 119878119895

10038161003816100381610038161003816+10038161003816100381610038161003816119874119894minus 119874119895

10038161003816100381610038161003816

(8)

where 120591119894119895(119905) represents the pheromone between manipulator

119894 and manipulator 119895 at 119905 moment 120578119894119895represents the visibility

(heuristic information) of transferring from manipulator 119894to manipulator 119895 V

119894119895represents the difference value of com-

prehensive weight between manipulator 119894 and manipulator119895 to the layout principles 120572 (120572 ge 0) represents the relativeimportance of 120591 120573 (120573 ge 0) represents the relative importanceof 120578 119880 represents the feasible point set namely the set ofmanipulators can be chosen by ant 119896 at 119905moment

(2) Pheromone andHeuristic InformationThis paper adoptedthe max-min ant system (MMAS) which was proposed bythe Belgium scholar Thomas Due to the combined withGA initialized pheromone was different fromMMAS At theinitial moment initial pheromone value is set as follows

120591119894119895(0) = 120591

119862+ 120591119866 (9)

where 120591119862is the given pheromone constant based on the

specific scale of solution equivalent to the 120591min in MMAS120591119866represents the value of pheromone converted from the

solution of GA

Computational Intelligence and Neuroscience 7

Table 1 Partition coding for manipulators

The name of the manipulator Code The name of the manipulator Code

Range 1

E-BRAKE 1

Range 3

LEFT CATHEAD 9GEN E-STOP 2 RIGHT CATHEAD 10VFD E-STOP 3 ROTARY CATHEAD 11

PARKING BRAKE 4 ALARM 12

Range 2

RT MOTOR CONTROL 5

Range 4

AIR SPINNER 13RT INERTIA BRAKE 6 TOOLS SELECT 14RT SPEED SETTING 7 SPARE1 15RT TORQUE LIMIT 8 SPARE2 16

Ant chose next manipulator mainly according topheromone 120591

119894119895(119905) and heuristic information 120578

119894119895 120578119894119895

wasgiven by the layout problem to be solved and affected by V

119894119895

which remained unchanged during the operation processin algorithm Ants released pheromone on the path theypassed by In order to avoid too much residual informationto submerge heuristic information residual informationshould be upgraded After ants traversed through all themanipulators the pheromone updated in the environmentThe update equation is as follows

120591119894119895(119905 + 1) = (1 minus 120588) sdot 120591

119894119895(119905) + Δ120591

119894119895(119905)

Δ120591119894119895(119905) = sumΔ120591

119896

119894119895(119905)

Δ120591119896

119894119895(119905) =

119876

119871119896

(ant 119896 passes though (119894 119895))

0 (otherwise)

(10)

where 120588 (0 le 120588 le 1) represents the volatilization coefficientof pheromone Δ120591119896

119894119895(119905) represents the amount of pheromone

released between manipulator 119894 and manipulator 119895 by ant 119896in this cycle Δ120591

119894119895(119905) represents the increment of pheromone

between manipulator 119894 and manipulator 119895 after this cycle 119876is a constant which represents the amount of pheromone 119871

119896

represents the objective function value corresponding withthe layout of all the manipulators formed by ant 119896 traversedthrough in this cycle

Only the ant with optimal layout scheme could updatepheromone in one cycle The amount of pheromone on eachpath would be limited in scope of [120591min 120591max] If beyondthis range the pheromone would be mandatory set as 120591minor 120591max Compared with the standard ACA the improvedalgorithm prevented premature stagnation With all theants completing the traverse of all the manipulators thepheromone accumulated and volatilized continuously Untilreaching the number of iterations or a certain fitness valuethe optimal path formed by ants was the best layout scheme

3 Application Verification

31 Layout Design of Driller Control Room on Drilling RigTake the layout design of human-machine interaction inter-face of driller control room on drilling rig as an exampleto illustrate the proposed method The 16 manipulators of

Table 2 The size of each type of manipulators (mm)

The types of manipulators Size The codes of manipulatorsSwitch knobs Φ30 4 5 6 9 10 11 12 13 14 15 16Rotary knobs Φ50 7 8Buttons Φ40 1 2 3

ZJ1209000DB rig console would be arranged (the grey areaas shown in Figure 5) First of all the 16 manipulators needto be encoded As shown in Table 1 when encoding layoutPrinciple 1 needs to be reflected that is manipulators withsimilar function will be arranged in one area to reduce thesearch time for operators

It is necessary to simplify the layout area and layoutobjects when describing themathematical model to facilitatea digital description of design variables According to thehuman upper limb dimension to determine the layout spaceand simplify the layout area as rectangular area (450 times

400mm) there are three types of manipulators includingbuttons rotary knobs and switch knobs Their sizes areslightly different and the specific sizes are shown in Table 2The spaces between manipulators are set as equal and theinterval is 100mm After being simplified the layout ofmanipulators can be regarded as a scheduling problem firstusing GA-ACA to find a good sorting and then according tothe order to establish the actual layout

Drilling process is mainly divided into three workingconditions pull out of hole run in hole and normal drillingConsidering the principle of operating sequence in multipleworking conditions determine the manipulatorsrsquo operatingsequence and calculate each manipulatorrsquos weight relativeto Principle 2 Through questionnaires to consult drillersdetermine the relative judgment matrix of the manipulatorsrsquoimportance and frequency and calculate each manipulatorrsquosweight relative to Principle 3 Analytic hierarchy process willbe used to calculate each manipulatorrsquos weight of 119879

119894and

119878119894 and the calculation result is shown in Table 3 Finally

determine the relative correlation between the manipulatorsThe correlation between manipulator and itself is expressedwith 1 and the correlation between themanipulator and othermanipulators is expressed in decimal within 0sim1 The greaterthe correlation between manipulators the greater the corre-lation value The correlation between the 16 manipulators isshown in Table 4

8 Computational Intelligence and Neuroscience

Table 3 The manipulatorsrsquo weight relative to Principles 2 and 3

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16119879119894

0018 0018 0018 0055 0113 0076 0205 0163 0068 0068 0068 0016 0016 0098 0 0119878119894

0163 0115 0115 0115 0069 0069 0069 0069 0043 0043 0043 0013 0026 0026 0011 0011

LEFT DRUMSHIFT LOCK

AIR SLIPS

LEFT DRUMCLUTCH

AIR HORN

AD MODE AD MOTOR

LEFT AD LEFT ADCLUTCH

TOP SCREENWIPER CONTROL SPEED SETTING

PROTECTION RESET

SILIENCERESET

DW SPEEDCONTROL

KEYBOARD12 13 3 2 9

101 4

5 7

6 8

11 14

15 16

PARKINGBRAKE

RT INERTIABRAKE

RT SPEEDSETTING

TOOLSSELECT

E-BRAKE

VFDE-STOP E-STOPGEN

RT MOTORCONTROL

RT TORQUELIMIT

LEFT

RIGHTCATHEAD

CATHEAD

ROTARYCATHEAD

ALARM

AIRSPINNER

SPARE2SPARE1

Armrest

WORKINGBRAKERIGHT DRUM

SHIFT LOCKRIGHT DRUMCLUTCH

RIGHT ADSHIFT LOCH

SHIFT LOCH

RIGHT ADCLUTCH

FRONT SCREENWIPER CONTROL

Number 2 PUMP

Number 1 PUMP Number 3 PUMP

SPEED SETTING

SPEED SETTING

Figure 5 Layout schematic diagram of ZJ1209000DB rig console (before optimization)

32 Optimization Result After completing the above datapreparation as shown in Figure 6 the operating parametersof GA and ACA need to be set up before the computeraided optimization calculation proceeds First set the controlparameters of GA population size 119873 = 50 crossover rate119901119888= 06 mutation rate 119901

119898= 02 and number of partition

119904 = 4 Second set the control parameters ofACAThe amountof ants is119898 = 10 120572 = 1 120573 = 1 and 120588 = 05

In the system of GA-ACA calculation module set theend condition of GA minimum genetic iterations Genmin =

15 maximum genetic iterations Genmax = 50 minimumevolution rate Gene

119901= 3 and Gene

119902= 3 Set the control

parameters of pheromone 119876 = 500 120591min = 10 and 120591max =

100 The optimal 10 of individuals in the last generationin GA would be selected as the solution set of geneticoptimization which would be transformed into pheromone

values If manipulator 119894 was adjacent to manipulator 119895 ingenetic optimization solution the pheromone added 10 onthe path (119894 119895) and all the initial values of pheromonesshould be set like this When meeting one of the followingconditions theACA should stop (1)Thenumber of iterationsreaches antmax = 50 (2) For continuous three generations inthe iteration the improvement rate of offspring optimizationis less than 05

Click the button of algorithm running on the systeminterface the system calculates via the program after 50times genetic iterations and 11 times ant colony optimizationiteration the value of the optimal layout scheme is 2822The sequence of the manipulators is obtained that is 4 13 2 6 5 7 8 9 10 12 11 14 13 15 and 16 Accordingto the optimized sequence the layout scheme is shown inFigure 7

Computational Intelligence and Neuroscience 9

Table 4 The correlation between the 16 manipulators

119874119894119895

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 161 1 05 07 09 0 0 0 0 0 0 0 0 0 0 0 02 05 1 09 05 0 0 0 0 0 0 0 0 0 0 0 03 07 09 1 07 0 0 0 0 0 0 0 0 0 0 0 04 09 05 07 1 0 0 0 0 0 0 0 0 0 0 0 05 0 0 0 0 1 09 07 09 0 0 0 0 0 0 0 06 0 0 0 0 09 1 05 07 0 0 0 0 0 0 0 07 0 0 0 0 07 05 1 09 0 0 0 0 0 0 0 08 0 0 0 0 09 07 09 1 0 0 0 0 0 0 0 09 0 0 0 0 0 0 0 0 1 09 07 05 0 0 0 010 0 0 0 0 0 0 0 0 09 1 07 05 0 0 0 011 0 0 0 0 0 0 0 0 07 07 1 05 0 0 0 012 0 0 0 0 0 0 0 0 05 05 05 1 0 0 0 013 0 0 0 0 0 0 0 0 0 0 0 0 1 09 03 0314 0 0 0 0 0 0 0 0 0 0 0 0 09 1 03 0315 0 0 0 0 0 0 0 0 0 0 0 0 03 03 1 0916 0 0 0 0 0 0 0 0 0 0 0 0 03 03 09 1

Table 5 The comparison between GA ACA and ACA

Algorithm Layout scheme The value of layout scheme Number of iterationsGA 4 2 3 1 7 8 6 5 12 11 9 10 13 14 15 16 2814 82ACA 4 1 2 3 5 6 7 8 9 10 11 12 15 16 14 13 2773 75GA-ACA 4 1 2 3 7 8 5 6 12 9 10 11 15 16 13 14 2835 50 + 13

Figure 6 System interface of layout design

Because the proposed method is still in the research anddevelopment there is certain difference between the solutionand the actual situation Besides the human cognitive activ-ities and layout experience cannot be completely describedby the layout model layout scheme obtained by algorithmoptimization is close to the optimal solution rather than theoptimal solution In this stage the designer needs to adjustthe layout scheme according to the actual needs Similarlysort the manipulators on the console at the left hand Forthe sake of evaluating layout scheme intuitively computersimulation design modeling is proceeded Through detaileddesign the final layout scheme is shown in Figure 8

The CATIA software will be used to evaluate the resultof the layout CATIA V5 integrates four ergonomic moduleswhich can evaluate visibility accessibility the comfort ofposture (analyze the angle of each joint) and so on The bluearea in Figure 9 shows the reach envelope of drillerrsquos righthand Under natural state the drillerrsquos arm can operate themain manipulators on console This shows that the locationsof manipulators were in the human bodyrsquos correspondingrange of joint motion and in accord with the armrsquos motiontrail In Figure 10 the virtual human model simulates oneoperating posture of driller And the postural score analysisin Figure 11 shows the comfort score of lumbar vertebrathoracic vertebra head arm and forearmThe sores indicatethat this operating posture is comfortable the position of thismanipulator matches with the movement characteristics ofthe human body

33 Algorithm Comparison Using single GA ACA and GA-ACA hybrid algorithm to solve the above layout optimizationproblem respectively set the number of test times as 10each Result comparison is shown in Table 5 the columnof layout scheme shows the optimal result in 10 timesrsquorunning and the third and the fourth columns show theaverage value of layout scheme and the average number ofiterations As can be seen from the test result no matterthe solution speed or precision the GA-ACA is superiorto single GA and ACA in solving layout optimizationproblem

10 Computational Intelligence and Neuroscience

GEN E-STOPVFDE-STOP

LEFT RIGHT

E-BRAKEPARKINGBRAKE

RT INERTIABRAKE

RT SPEEDSETTING LIMIT

RT MOTORCONTROL

RT TORQUE ROTARYCATHEAD

CATHEAD CATHEAD

AIR SPARE1 SPARE2

ALARM

TOOLSSELECT

WORKINGBRAKE

Armrest0

200

0 200 400 600 800 10002004006008001000

400

600

800

Comfortableoperating range

operating rangeMaximum

1 3 24

56

7 8

9 10

1112

1314 15 16

SPINNER

Range 1

Range 2 Range 3

Range 4

y

x

Figure 7 Layout scheme of right console (after optimization)

Figure 8 Modeling and simulation of layout scheme

Figure 9 Analyze the right handrsquos reach envelope

34 Discussion Through reference to previous researchabout cognitive model this paper integrated their advantagesand put forward the cognitive model of human-machineinteraction interface suitable for cabin This model describedthe cognitive process of operators working in the cabinAnd then by analyzing the information processing processthe layout principles of human-machine interaction interfacewere summed up which would be quantified in the objectivefunction However human cognitive behavior process is verycomplicated part of the cognitive activities is difficult toexpress by formalized methods layout principles needed tobe improved further There were lots of influence factors in

Figure 10 The simulation of operating posture

Figure 11 The comfort evaluation of operating posture

actual engineering problem the layout problem was difficultto be described completely by mathematical model

It has become common that the bionic intelligencealgorithms were applied in the layout optimization designbut cognitive characteristics were seldom considered as theconstraints of layout optimization algorithm FurthermoreGA-ACA combined the advantages of two algorithms whichwould be more precise to solve layout optimization problemThe layout design of human-machine interaction interfaceof driller control room on drilling rig has illustrated theproposed method According to the characteristics of thehuman-machine interaction interface of different product

Computational Intelligence and Neuroscience 11

this method could be modified and adapted As a layoutergonomic design method it could assist designers andengineers to conduct human-machine interaction interfacedesign and improve the efficiency in layout design

4 Conclusion

Based on the theory of cognitive psychology according toWickens information processing model the cognitive modelof human-machine interaction interface was establishedConsidering peoplersquos information organization law visualsearch law user memory characteristics and so on thehuman-machine interaction interface should conform tooperatorsrsquo cognitive ability for interface information Basedon the cognitive model the layout principles of human-machine interaction interface were summarized as the layoutconstraintsThe problem of solving layout scheme was trans-formed into combinatorial optimization problem and GA-ACA was put forward to solve the problem which realizedthe algorithmization of artificial optimization process

Taking the layout of manipulators as example the objec-tive function of layout optimization was built according toeach principle Use GA to generate the solution of layoutscheme which would be transformed into initial pheromoneas ACA required Taking the difference value of comprehen-sive weight of the manipulators for the layout principles asheuristic information ofACA combinedwith the pheromoneprovided by GA the positive feedback optimization mecha-nism of ACA was used to solve further GA-ACA integratedthe complementary advantages of GA and ACA which hadgood optimizing performance and time performance andimproved the design efficiency Taking the 16 manipulators ofZJ1209000DB rig console as layout example layout designsystem of human-computer interaction interface for cabinwas developed by Visual Basic which validated the abovelayout optimization method

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgments

This research was supported by Open Fund (OGE201403-23) of Key Laboratory of Oil amp Gas Equipment Ministryof Education (Southwest Petroleum University) and by theOpen Research Subject (GY-14YB-31) of Key Laboratory(Research Base) of Industrial Design Industry Research Cen-ter Humanities and Social Science EducationDepartment ofSichuan Province

References

[1] J Z Cha X J Tang and Y P Lu ldquoSurvey on packing problemsrdquoJournal of Computer-AidedDesignampComputer Graphics vol 14pp 705ndash711 2002

[2] K A Dowsland and W B Dowsland ldquoPacking problemsrdquoEuropean Journal of Operational Research vol 56 no 1 pp 2ndash14 1992

[3] S Margaritis and NMarmaras ldquoSupporting the design of officelayout meeting ergonomics requirementsrdquo Applied Ergonomicsvol 38 no 6 pp 781ndash790 2007

[4] N Shariatzadeh G Sivard and D Chen ldquoSoftware evalu-ation criteria for rapid factory layout planning design andsimulationrdquo in Proceedings of the 45th CIRP Conference onManufacturing Systems pp 299ndash304 Athens GreeceMay 2012

[5] J Z Huo G Q Li H F Teng and Z G Sun ldquoHuman-computercooperative ant colonygenetic algorithm for satellite modulelayout designrdquo Chinese Journal of Mechanical Engineering vol41 no 3 pp 112ndash116 2005

[6] R Li D M Zhuang R Wang and L R Wang ldquoOptimizationabout the layout work of the steering arrangement in thecockpitrdquo Journal of System Simulation vol 16 pp 1305ndash13072004

[7] S Y Yan Y Chen and L Y Liang ldquoOptimization of controlslayout based on simulated annealing algorithmrdquo Nuclear PowerEngineering vol 35 no 1 pp 67ndash70 2014

[8] L C Zong C Ye S H Yu and D K Chen ldquoResearchand application of intelligent layout method in DSV cabinequipmentrdquo Shipbuilding of China vol 54 no 3 pp 147ndash1542013

[9] L C Zong S H Yu J B Sun L W Han and S S An ldquoStudyon cabin layout optimization with fish algorithmrdquo MechanicalScience and Technology for Aerospace Engineering vol 33 pp257ndash262 2014

[10] Y L Wang C Wang Z S Ji and X G Zhao ldquoA study onintelligent layout design of ship cabinrdquo Shipbuilding of Chinavol 54 pp 139ndash146 2013

[11] D HWolpert andWGMacready ldquoNo free lunch theorems foroptimizationrdquo IEEE Transactions on Evolutionary Computationvol 1 no 1 pp 67ndash82 1997

[12] F Q Zhao G Q Li C Yang A Abraham and H B LiuldquoA human-computer cooperative particle swarm optimizationbased immune algorithm for layout designrdquo Neurocomputingvol 132 pp 68ndash78 2014

[13] A Lodi S Martello and M Monaci ldquoTwo-dimensionalpacking problems a surveyrdquo European Journal of OperationalResearch vol 141 no 2 pp 241ndash252 2002

[14] J R AndersonCognitive Psychology and Itrsquos Implications editedby Y L Qin Posts amp Telecom Press 2012

[15] A Ghanbari S M R Kazemi F Mehmanpazir and M MNakhostin ldquoA cooperative ant colony optimization-geneticalgorithm approach for construction of energy demand fore-casting knowledge-based expert systemsrdquo Knowledge-BasedSystems vol 39 pp 194ndash206 2013

[16] J B Best Cognitive Psychology X T Huang Ed China LightIndustry Press Beijing China 2000

[17] C Wickens J G Hollands S Banbury and R ParasuramanEngineering Psychology amp Human Performance Pearson Lon-don UK 4th edition 2012

[18] L Y Sun Z X Li and T S Jin ldquoCognitive synthetic model andits application in HCIrdquo Journal of Xirsquoan Jiaotong University vol31 pp 74ndash80 1997

[19] Y P Zhang ldquoHuman-computer interface design based onknowledge of cognitive psychologyrdquo Computer Engineering andApplications vol 30 pp 105ndash107 2005

12 Computational Intelligence and Neuroscience

[20] Z Lan ldquoThe cognitive basis of software interface designrdquo ShanxiScience and Technology no 3 pp 41ndash42 1998

[21] X P Wang and L M Cao Genetic AlgorithmmdashTheory Appli-cation and Software Implementation Xirsquoan Jiaotong UniversityPress Xirsquoan China 2002

[22] E Bonabeau M Dorigo and G Theraulaz ldquoInspiration foroptimization from social insect behaviourrdquoNature vol 406 no6791 pp 39ndash42 2000

[23] M Dorigo and L M Gambardella ldquoAnt colonies for thetravelling salesman problemrdquo BioSystems vol 43 no 2 pp 73ndash81 1997

[24] MDorigo VManiezzo andA Colorni ldquoAnt system optimiza-tion by a colony of cooperating agentsrdquo IEEE Transactions onSystems Man and Cybernetics Part B Cybernetics vol 26 no1 pp 29ndash41 1996

[25] J-L Ding Z-Q Chen and Z-Z Yuan ldquoOn the Markovconvergence analysis for the combination of genetic algorithmand ant algorithmrdquo Acta Automatica Sinica vol 30 no 4 pp629ndash634 2004

[26] Z-J Lee S-F Su C-C Chuang and K-H Liu ldquoGeneticalgorithmwith ant colony optimization (GA-ACO) formultiplesequence alignmentrdquo Applied Soft Computing vol 8 no 1 pp55ndash78 2008

[27] Z Li C P Xu W Mo and G J Chen ldquoInitialization forsynchronous sequential circuits based on ant algorithm ampgenetic algorithmrdquo Acta Electronica Sinica vol 31 pp 1276ndash1280 2003

[28] Z-H Xiong S-K Li and J-H Chen ldquoHardwaresoftware par-titioning based on dynamic combination of genetic algorithmand ant algorithmrdquo Journal of Software vol 16 no 4 pp 503ndash512 2005

[29] J-L Ding Z-Q Chen and Z-Z Yuan ldquoOn the combinationof genetic algorithm and ant algorithmrdquo Journal of ComputerResearch and Development vol 40 no 9 pp 1351ndash1356 2003

[30] T Stutzle and H H Hoos ldquoMAX-MIN ant systemrdquo FutureGeneration Computer Systems vol 16 no 8 pp 889ndash914 2000

[31] T Stuetzle and H Hoos ldquoMAX-MIN Ant System and localsearch for the traveling salesman problemrdquo in Proceedings ofthe IEEE International Conference on Evolutionary Computation(ICEC rsquo97) pp 309ndash314 Indianapolis Indiana April 1997

Submit your manuscripts athttpwwwhindawicom

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Applied Computational Intelligence and Soft Computing

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Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014

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Human-ComputerInteraction

Advances in

Computer EngineeringAdvances in

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4 Computational Intelligence and Neuroscience

Principle 1 Cognition corresponds to objective

According to the functional partition

Principle 2 Task flow design

According to the operating sequence to arrange

Principle 3 Human-machineinteraction

interface matches with the usersrsquo cognitive

strategy

According to the importance and operating frequency to arrange

Principle 4 In accordance with

information organization law

According to the correlation to arrange

Arranged in one area when coding

Fitness function

Ti

Si

Oij

Figure 3 The process of building objective function

search the manipulator at the familiar area of interface in anautomatic way

Principle 2 (task flow design) Through the operatingsequence design to reduce the usersrsquo workload and improvethe working efficiency in view of the main tasks of theoperator that need to be done the execution process oftasks should be analyzed And then according to operatorsrsquocognitive habits to merge or reduce the unnecessary actionsor implement automation so as to simplify the dialogueprocess of human-machine interaction which would speedup the information processing and reduce the usersrsquo cognitiveload and cognitive time in information processing

Principle 3 (human-machine interaction interface matcheswith the usersrsquo cognitive strategy) Human cognition haddynamic characteristic the human-machine interactioninterface was very difficult to adapt to the dynamic cognitivedifferences of all kinds of users So based on usersrsquo knowledgelevel cognitive ability and habits meanwhile by judging thedifferent levels of usersrsquo cognitive strategy human-machineinteraction interface would be designed intelligently so as toadapt to the usersrsquo cognitive process dynamically For exam-ple according to the characteristics ofmemory the importantdisplay andmanipulator whichwould be frequently observedandmanipulated should be arranged in the convenient rangefor observation and comfortable range for manipulationTheburden on the usersrsquo short-term memory could be easedAnd it was advantageous to reasonably distribute cognitiveload between layers of intuition template and reasoning andavoiding forgetting and memory errors such as consequence

Principle 4 (in accordance with information organizationlaw) The discrete stimulate in view could be organizedtogether and formed the vision of a whole by the certainrelationship between them and this phenomenonwas knownas the visual organization features Visual organizationalprinciples mainly included proximity principle similarity

principle and closeness principle which had a certain guid-ing significance in the interface design For example supposethere was lots of information that needed to be displayed onthe screen in order tomake the information displayed clearlythe relevant information should be put together by proximityprinciple or be expressed in the same color by similarityprinciple So this information seemed to be whole and theusers could detect them rapidly and accurately

24 Determine the Optimization Objective according to theLayout Principles The human-machine interaction interfacelayout problem could be transformed into the combinatorialoptimization problem that is the layout scheme whichmost met the layout principles was sought from differentpermutation and combination scheme of the layout objectsThe optimization objectivewas to find the best layout schememaking the objective function get the maximum value Theprocess of building objective function is shown in Figure 3

In short layout Principle 1 embodied in the layout objectswith similar function will be arranged in one area LayoutPrinciple 2 is embodied in the arrangement in accordancewith the physical activities characteristics from left to rightand from top to bottom Layout Principle 3 embodied in theimportant layout objects will be arranged near the best layoutpoint 119896 Layout Principle 4 embodied in the related layoutobjects will be arranged close to each other

According to the layout principles the objective functionis defined as follows

119891 (119894)best

= argmax[

[

119899

sum

119894=1

1205751sdot 119879119894+

119899

sum

119894=1

1205752sdot 119878119894+

119899

sum

119894=1

119899

sum

119895=119894+1

119874119894119895

119889119894119895

]

]

(1)

1205751= 1 minus

119894

119899 (2)

1205752= 1 minus

119889119894119896

119889119896119898

(3)

Computational Intelligence and Neuroscience 5

SelectCross

Mutate

ttat0

AAGA

N+ 1

N + 1Define the target and the fitness function

Generate a number of optimal solutions

The layout design of human-machineinteraction interface

Output the optimal solution

Update the pheromone of each route

m ants traversal n nodes after the ants pass throughaccording to the optimal route to increase the pheromone

Calculate the probability of each ant moving to the next nodethen according to probability to move the ants to the next node

Initialization parameters take the optimal solution generated by GAas the distribution of initial pheromoneput m ants on n nodes

a

Figure 4 The process of GA-ACA

where 119879119894represents the weight of layout object 119894 relative

to Principle 2 119878119894represents the weight of layout object 119894

relative to Principle 3 119874119894119895represents the correlation between

layout object 119894 and layout object 119895 relative to Principle4 119889119894119895represents the distance between layout object 119894 and

layout object 119895 119899 represents the number of layout objects 119894represents the position number of layout objects 120575

1and 120575

2

represent the position control coefficients 119889119894119896represents the

distance between the layout object 119894 and the best layout point119896 119889119896119898

represents the maximum distance between a layoutpoint and the best layout point 119896 in the whole layout range

25 Establish the Layout Design Model of Human-MachineInteraction Interface Based on GA-ACA

251 GA-ACA In 1975 according to Darwinrsquos theory ofsurvival of the fittest the American scholar John Hollandput forward the genetic algorithm [21] Through parallel andglobal search mode to search the best individual in the opti-mization group GA had good adaptive ability robustnessgenerality and othermerits widely used inmachine learningpattern recognition image processing and other fields

In the early 1990s inspired from antsrsquo foraging behaviorin the nature world the Italian scholar Dorigo et al presentedant colony algorithm [22ndash24] ACA had very strong robust-ness and ability to search a good solution and easy to parallelimplementation Originally it is used to solve the travelingsalesman problem latter widely applied to solve the classicaloptimization problem such as sequential ordering problemand quadratic assignment problem

GA and ACA were stochastic optimization algorithmboth of which had the advantages of global search andrandom search and easily combined with other algorithmsHowever GA had the shortcomings of huge calculation andpoor stability leading to low precision and efficiency ACAneeded long search time and is easily prone to stagnationphenomenonThe combination of GA and ACA could utilizethe advantage of the two algorithms and overcome theirdisadvantages and the research has proved that the hybridalgorithm had good efficiency [25] This new algorithm wasused for multiple sequence alignment [26] initializationfor synchronous sequential circuits [27] hardwaresoftwarepartitioning [28] and other optimization problems

According to the study and experiment of GA and ACA[29] the speed-time curve is shown in Figure 4 In the earlystages of the search (119905

0sim 119905119886) GA has fast convergence When

evolution reaches a certain degree its evolutionary ratewouldfall sharply namely the efficiency is low after 119905

119886 Instead at

the beginning of the search (1199050sim 119905119886) ACA searches slowly

But after the accumulation of pheromone reaches a certainextent the searching activity of ants can present a certainregularity that is to say the speed is rapidly improved after119905119886As shown in Figure 4 this paper put forward combining

the advantages of the two algorithms in layout design GAis adopted in the former process of algorithm (before point119886) and the rapidity randomness and global convergence ofGA are used A number of layout schemes of human-machineinteraction interface are generated as initial solution whichwill be turned into track intensity distribution as initializationpheromone Then ACA is adopted to optimize the layoutschemes in the latter process of algorithm (after point119886) In the case of a certain track intensity distribution ofinitialization pheromone the characteristics of parallelismpositive feedbackmechanism and high solving efficiencywillbe used to improve the efficiency of solving and output theoptimal solution

252 GA to Determine the Initial Pheromone

(1) The Operating Mechanism of GA The process of GA inhuman-machine interaction interface layout is shown at theleft part in Figure 4

(a) Coding Using the form of sequence coding the layoutobjects were expressed by real number to carry out optimiz-ing calculation The code string format shows as follows

119862119898= [119888119898

1 119888119898

2 119888119898

3 119888

119898

119899] (4)

where 119898 represents the population size 119899 represents thenumber of the layout objects 119888119898

119899represents the layout object

in the 119899th position of the119898th chromosomeSuppose there are 10 layout objects which randomly

generated an individual such as [5 4 3 8 7 2 10 1 9 6]Thecode string represents layout object 5 in the first positionlayout object 4 in the second position and so on There are

6 Computational Intelligence and Neuroscience

direct relationships between the objective function and thecoordinate of layout objects If the codes of layout objectsare in different position the corresponding coordinatesare different and the objective function value will also bedifferent

(b) Construct the Fitness Function Take each kind of permu-tation and combination way as an individual and then thebest layout solution was sought by calculating the individualrsquosfitness value and evolutionary operationThe119901th fitness valueof chromosome is as follows

fitness (119901) =119899

sum

119894=1

1205751sdot 119879119894+

119899

sum

119894=1

1205752sdot 119878119894+

119899

sum

119894=1

119899

sum

119895=119894+1

119874119894119895

119889119894119895

(5)

The three parts of fitness function reflect the comprehen-sive situation of layout Principles 2 3 and 4 respectivelythe pros and cons of layout schemes will be judged from theoverall layout

(c) Determine the Evolutionary Mechanism It is done startingfrom the random generation of initial population constantlyrepeating the process of selection crossover and mutationoperation and the population developed along the directionof established goals from generation to generation Accordingto the fitness value of the fitness function excellent indi-viduals would be selected from the current population bywheel bet method so as to form a new population Usingsequence crossover method parent individuals exchangedpart of genes and thus new individuals would be formedUsing swapmutationmethod the diversity of populationwasguaranteed and immature convergence phenomenon wasprevented

(2) The Combination of GA and ACA The combinationopportunity of GA and ACA was dynamically determinedin the operational process of GA First set minimum geneticiterations Genmin andmaximum genetic iterations Genmax inGAThen according to formula (6) record the evolution rateof progeny population in the iterative process and set theminimumevolution rateGen

119901of progeny populationWithin

the scope of the given number of iterations if successiveGen119902generations the evolution rate of progeny population

were less than Gen119901 this showed that the optimization speed

was very low The process of GA should be terminated andturned into the ACA The 10 of the optimal individuals inlast generation in GA would be selected as the carrier whichwould be taken as the suboptimal solution to distribute theinitial pheromone in ACA and optimal solution would beobtained further

EV119896 =sum119898

119894=1(119891119896

119894minus 119891119896minus1

119894) 119891119896minus1

119894

119898 119896 = 2 3 119899 (6)

where 119898 represents population size 119896 represents evolutionalgeneration 119891119896

119894represents the fitness value of individual 119894 in

119896th iteration

253 ACA to Solve Pareto Solution The purpose of layoutoptimization of human-machine interaction interface was to

get an optimal solution to satisfy the design goal which wasto find an optimal path from layout object 119894 to layout object119895 and the optimized goal was to get a maximum of (1) Inthe ACA system model each feasible solution represented apath that an ant walked by It is a shortest path problem sothe objective function of ACA was defined as the reciprocalof the objective function of GA

119871119896=

1

119891 (119894)best (7)

(1) State Transition Rules The process of ACA in human-machine interaction interface layout is shown at the right partin Figure 4 Take the layout of manipulators to describe therealization process of ACA [30 31] Set 119887

119894(119905) (119894 = 1 2 119899)

represents the number of ants of manipulator 119894 in time 119905119898 =

sum119899

119894=1119887119894(119905) represents the total number of ants and 119899 represents

the number of manipulators Set tabu119896represents the tabu

table of ant 119896 which will be adjusted dynamically along withthe ant optimization process In initial stage 119898 ants willbe placed on 119899 manipulators randomly The first elementof each antrsquos tabu table will be set as its first manipulatorIn the process of each iteration each ant chooses the nextmanipulator by the state transition rules repeatedly Afterselection for 119899minus1 times a group ofmanipulators layoutwill begenerated eventually The probability of ant 119896 transfers frommanipulator 119894 to manipulator 119895 in time 119905 is as follows

119901119896

119894119895(119905) =

[120591119894119895(119905)]120572

[120578119894119895]120573

sum119897isin119880

[120591119894119897(119905)]120572

[120578119894119897]120573

119895 isin 119880

0 119895 notin 119880

120578119894119895=

1

V119894119895

V119894119895=10038161003816100381610038161003816119879119894minus 119879119895

10038161003816100381610038161003816+10038161003816100381610038161003816119878119894minus 119878119895

10038161003816100381610038161003816+10038161003816100381610038161003816119874119894minus 119874119895

10038161003816100381610038161003816

(8)

where 120591119894119895(119905) represents the pheromone between manipulator

119894 and manipulator 119895 at 119905 moment 120578119894119895represents the visibility

(heuristic information) of transferring from manipulator 119894to manipulator 119895 V

119894119895represents the difference value of com-

prehensive weight between manipulator 119894 and manipulator119895 to the layout principles 120572 (120572 ge 0) represents the relativeimportance of 120591 120573 (120573 ge 0) represents the relative importanceof 120578 119880 represents the feasible point set namely the set ofmanipulators can be chosen by ant 119896 at 119905moment

(2) Pheromone andHeuristic InformationThis paper adoptedthe max-min ant system (MMAS) which was proposed bythe Belgium scholar Thomas Due to the combined withGA initialized pheromone was different fromMMAS At theinitial moment initial pheromone value is set as follows

120591119894119895(0) = 120591

119862+ 120591119866 (9)

where 120591119862is the given pheromone constant based on the

specific scale of solution equivalent to the 120591min in MMAS120591119866represents the value of pheromone converted from the

solution of GA

Computational Intelligence and Neuroscience 7

Table 1 Partition coding for manipulators

The name of the manipulator Code The name of the manipulator Code

Range 1

E-BRAKE 1

Range 3

LEFT CATHEAD 9GEN E-STOP 2 RIGHT CATHEAD 10VFD E-STOP 3 ROTARY CATHEAD 11

PARKING BRAKE 4 ALARM 12

Range 2

RT MOTOR CONTROL 5

Range 4

AIR SPINNER 13RT INERTIA BRAKE 6 TOOLS SELECT 14RT SPEED SETTING 7 SPARE1 15RT TORQUE LIMIT 8 SPARE2 16

Ant chose next manipulator mainly according topheromone 120591

119894119895(119905) and heuristic information 120578

119894119895 120578119894119895

wasgiven by the layout problem to be solved and affected by V

119894119895

which remained unchanged during the operation processin algorithm Ants released pheromone on the path theypassed by In order to avoid too much residual informationto submerge heuristic information residual informationshould be upgraded After ants traversed through all themanipulators the pheromone updated in the environmentThe update equation is as follows

120591119894119895(119905 + 1) = (1 minus 120588) sdot 120591

119894119895(119905) + Δ120591

119894119895(119905)

Δ120591119894119895(119905) = sumΔ120591

119896

119894119895(119905)

Δ120591119896

119894119895(119905) =

119876

119871119896

(ant 119896 passes though (119894 119895))

0 (otherwise)

(10)

where 120588 (0 le 120588 le 1) represents the volatilization coefficientof pheromone Δ120591119896

119894119895(119905) represents the amount of pheromone

released between manipulator 119894 and manipulator 119895 by ant 119896in this cycle Δ120591

119894119895(119905) represents the increment of pheromone

between manipulator 119894 and manipulator 119895 after this cycle 119876is a constant which represents the amount of pheromone 119871

119896

represents the objective function value corresponding withthe layout of all the manipulators formed by ant 119896 traversedthrough in this cycle

Only the ant with optimal layout scheme could updatepheromone in one cycle The amount of pheromone on eachpath would be limited in scope of [120591min 120591max] If beyondthis range the pheromone would be mandatory set as 120591minor 120591max Compared with the standard ACA the improvedalgorithm prevented premature stagnation With all theants completing the traverse of all the manipulators thepheromone accumulated and volatilized continuously Untilreaching the number of iterations or a certain fitness valuethe optimal path formed by ants was the best layout scheme

3 Application Verification

31 Layout Design of Driller Control Room on Drilling RigTake the layout design of human-machine interaction inter-face of driller control room on drilling rig as an exampleto illustrate the proposed method The 16 manipulators of

Table 2 The size of each type of manipulators (mm)

The types of manipulators Size The codes of manipulatorsSwitch knobs Φ30 4 5 6 9 10 11 12 13 14 15 16Rotary knobs Φ50 7 8Buttons Φ40 1 2 3

ZJ1209000DB rig console would be arranged (the grey areaas shown in Figure 5) First of all the 16 manipulators needto be encoded As shown in Table 1 when encoding layoutPrinciple 1 needs to be reflected that is manipulators withsimilar function will be arranged in one area to reduce thesearch time for operators

It is necessary to simplify the layout area and layoutobjects when describing themathematical model to facilitatea digital description of design variables According to thehuman upper limb dimension to determine the layout spaceand simplify the layout area as rectangular area (450 times

400mm) there are three types of manipulators includingbuttons rotary knobs and switch knobs Their sizes areslightly different and the specific sizes are shown in Table 2The spaces between manipulators are set as equal and theinterval is 100mm After being simplified the layout ofmanipulators can be regarded as a scheduling problem firstusing GA-ACA to find a good sorting and then according tothe order to establish the actual layout

Drilling process is mainly divided into three workingconditions pull out of hole run in hole and normal drillingConsidering the principle of operating sequence in multipleworking conditions determine the manipulatorsrsquo operatingsequence and calculate each manipulatorrsquos weight relativeto Principle 2 Through questionnaires to consult drillersdetermine the relative judgment matrix of the manipulatorsrsquoimportance and frequency and calculate each manipulatorrsquosweight relative to Principle 3 Analytic hierarchy process willbe used to calculate each manipulatorrsquos weight of 119879

119894and

119878119894 and the calculation result is shown in Table 3 Finally

determine the relative correlation between the manipulatorsThe correlation between manipulator and itself is expressedwith 1 and the correlation between themanipulator and othermanipulators is expressed in decimal within 0sim1 The greaterthe correlation between manipulators the greater the corre-lation value The correlation between the 16 manipulators isshown in Table 4

8 Computational Intelligence and Neuroscience

Table 3 The manipulatorsrsquo weight relative to Principles 2 and 3

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16119879119894

0018 0018 0018 0055 0113 0076 0205 0163 0068 0068 0068 0016 0016 0098 0 0119878119894

0163 0115 0115 0115 0069 0069 0069 0069 0043 0043 0043 0013 0026 0026 0011 0011

LEFT DRUMSHIFT LOCK

AIR SLIPS

LEFT DRUMCLUTCH

AIR HORN

AD MODE AD MOTOR

LEFT AD LEFT ADCLUTCH

TOP SCREENWIPER CONTROL SPEED SETTING

PROTECTION RESET

SILIENCERESET

DW SPEEDCONTROL

KEYBOARD12 13 3 2 9

101 4

5 7

6 8

11 14

15 16

PARKINGBRAKE

RT INERTIABRAKE

RT SPEEDSETTING

TOOLSSELECT

E-BRAKE

VFDE-STOP E-STOPGEN

RT MOTORCONTROL

RT TORQUELIMIT

LEFT

RIGHTCATHEAD

CATHEAD

ROTARYCATHEAD

ALARM

AIRSPINNER

SPARE2SPARE1

Armrest

WORKINGBRAKERIGHT DRUM

SHIFT LOCKRIGHT DRUMCLUTCH

RIGHT ADSHIFT LOCH

SHIFT LOCH

RIGHT ADCLUTCH

FRONT SCREENWIPER CONTROL

Number 2 PUMP

Number 1 PUMP Number 3 PUMP

SPEED SETTING

SPEED SETTING

Figure 5 Layout schematic diagram of ZJ1209000DB rig console (before optimization)

32 Optimization Result After completing the above datapreparation as shown in Figure 6 the operating parametersof GA and ACA need to be set up before the computeraided optimization calculation proceeds First set the controlparameters of GA population size 119873 = 50 crossover rate119901119888= 06 mutation rate 119901

119898= 02 and number of partition

119904 = 4 Second set the control parameters ofACAThe amountof ants is119898 = 10 120572 = 1 120573 = 1 and 120588 = 05

In the system of GA-ACA calculation module set theend condition of GA minimum genetic iterations Genmin =

15 maximum genetic iterations Genmax = 50 minimumevolution rate Gene

119901= 3 and Gene

119902= 3 Set the control

parameters of pheromone 119876 = 500 120591min = 10 and 120591max =

100 The optimal 10 of individuals in the last generationin GA would be selected as the solution set of geneticoptimization which would be transformed into pheromone

values If manipulator 119894 was adjacent to manipulator 119895 ingenetic optimization solution the pheromone added 10 onthe path (119894 119895) and all the initial values of pheromonesshould be set like this When meeting one of the followingconditions theACA should stop (1)Thenumber of iterationsreaches antmax = 50 (2) For continuous three generations inthe iteration the improvement rate of offspring optimizationis less than 05

Click the button of algorithm running on the systeminterface the system calculates via the program after 50times genetic iterations and 11 times ant colony optimizationiteration the value of the optimal layout scheme is 2822The sequence of the manipulators is obtained that is 4 13 2 6 5 7 8 9 10 12 11 14 13 15 and 16 Accordingto the optimized sequence the layout scheme is shown inFigure 7

Computational Intelligence and Neuroscience 9

Table 4 The correlation between the 16 manipulators

119874119894119895

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 161 1 05 07 09 0 0 0 0 0 0 0 0 0 0 0 02 05 1 09 05 0 0 0 0 0 0 0 0 0 0 0 03 07 09 1 07 0 0 0 0 0 0 0 0 0 0 0 04 09 05 07 1 0 0 0 0 0 0 0 0 0 0 0 05 0 0 0 0 1 09 07 09 0 0 0 0 0 0 0 06 0 0 0 0 09 1 05 07 0 0 0 0 0 0 0 07 0 0 0 0 07 05 1 09 0 0 0 0 0 0 0 08 0 0 0 0 09 07 09 1 0 0 0 0 0 0 0 09 0 0 0 0 0 0 0 0 1 09 07 05 0 0 0 010 0 0 0 0 0 0 0 0 09 1 07 05 0 0 0 011 0 0 0 0 0 0 0 0 07 07 1 05 0 0 0 012 0 0 0 0 0 0 0 0 05 05 05 1 0 0 0 013 0 0 0 0 0 0 0 0 0 0 0 0 1 09 03 0314 0 0 0 0 0 0 0 0 0 0 0 0 09 1 03 0315 0 0 0 0 0 0 0 0 0 0 0 0 03 03 1 0916 0 0 0 0 0 0 0 0 0 0 0 0 03 03 09 1

Table 5 The comparison between GA ACA and ACA

Algorithm Layout scheme The value of layout scheme Number of iterationsGA 4 2 3 1 7 8 6 5 12 11 9 10 13 14 15 16 2814 82ACA 4 1 2 3 5 6 7 8 9 10 11 12 15 16 14 13 2773 75GA-ACA 4 1 2 3 7 8 5 6 12 9 10 11 15 16 13 14 2835 50 + 13

Figure 6 System interface of layout design

Because the proposed method is still in the research anddevelopment there is certain difference between the solutionand the actual situation Besides the human cognitive activ-ities and layout experience cannot be completely describedby the layout model layout scheme obtained by algorithmoptimization is close to the optimal solution rather than theoptimal solution In this stage the designer needs to adjustthe layout scheme according to the actual needs Similarlysort the manipulators on the console at the left hand Forthe sake of evaluating layout scheme intuitively computersimulation design modeling is proceeded Through detaileddesign the final layout scheme is shown in Figure 8

The CATIA software will be used to evaluate the resultof the layout CATIA V5 integrates four ergonomic moduleswhich can evaluate visibility accessibility the comfort ofposture (analyze the angle of each joint) and so on The bluearea in Figure 9 shows the reach envelope of drillerrsquos righthand Under natural state the drillerrsquos arm can operate themain manipulators on console This shows that the locationsof manipulators were in the human bodyrsquos correspondingrange of joint motion and in accord with the armrsquos motiontrail In Figure 10 the virtual human model simulates oneoperating posture of driller And the postural score analysisin Figure 11 shows the comfort score of lumbar vertebrathoracic vertebra head arm and forearmThe sores indicatethat this operating posture is comfortable the position of thismanipulator matches with the movement characteristics ofthe human body

33 Algorithm Comparison Using single GA ACA and GA-ACA hybrid algorithm to solve the above layout optimizationproblem respectively set the number of test times as 10each Result comparison is shown in Table 5 the columnof layout scheme shows the optimal result in 10 timesrsquorunning and the third and the fourth columns show theaverage value of layout scheme and the average number ofiterations As can be seen from the test result no matterthe solution speed or precision the GA-ACA is superiorto single GA and ACA in solving layout optimizationproblem

10 Computational Intelligence and Neuroscience

GEN E-STOPVFDE-STOP

LEFT RIGHT

E-BRAKEPARKINGBRAKE

RT INERTIABRAKE

RT SPEEDSETTING LIMIT

RT MOTORCONTROL

RT TORQUE ROTARYCATHEAD

CATHEAD CATHEAD

AIR SPARE1 SPARE2

ALARM

TOOLSSELECT

WORKINGBRAKE

Armrest0

200

0 200 400 600 800 10002004006008001000

400

600

800

Comfortableoperating range

operating rangeMaximum

1 3 24

56

7 8

9 10

1112

1314 15 16

SPINNER

Range 1

Range 2 Range 3

Range 4

y

x

Figure 7 Layout scheme of right console (after optimization)

Figure 8 Modeling and simulation of layout scheme

Figure 9 Analyze the right handrsquos reach envelope

34 Discussion Through reference to previous researchabout cognitive model this paper integrated their advantagesand put forward the cognitive model of human-machineinteraction interface suitable for cabin This model describedthe cognitive process of operators working in the cabinAnd then by analyzing the information processing processthe layout principles of human-machine interaction interfacewere summed up which would be quantified in the objectivefunction However human cognitive behavior process is verycomplicated part of the cognitive activities is difficult toexpress by formalized methods layout principles needed tobe improved further There were lots of influence factors in

Figure 10 The simulation of operating posture

Figure 11 The comfort evaluation of operating posture

actual engineering problem the layout problem was difficultto be described completely by mathematical model

It has become common that the bionic intelligencealgorithms were applied in the layout optimization designbut cognitive characteristics were seldom considered as theconstraints of layout optimization algorithm FurthermoreGA-ACA combined the advantages of two algorithms whichwould be more precise to solve layout optimization problemThe layout design of human-machine interaction interfaceof driller control room on drilling rig has illustrated theproposed method According to the characteristics of thehuman-machine interaction interface of different product

Computational Intelligence and Neuroscience 11

this method could be modified and adapted As a layoutergonomic design method it could assist designers andengineers to conduct human-machine interaction interfacedesign and improve the efficiency in layout design

4 Conclusion

Based on the theory of cognitive psychology according toWickens information processing model the cognitive modelof human-machine interaction interface was establishedConsidering peoplersquos information organization law visualsearch law user memory characteristics and so on thehuman-machine interaction interface should conform tooperatorsrsquo cognitive ability for interface information Basedon the cognitive model the layout principles of human-machine interaction interface were summarized as the layoutconstraintsThe problem of solving layout scheme was trans-formed into combinatorial optimization problem and GA-ACA was put forward to solve the problem which realizedthe algorithmization of artificial optimization process

Taking the layout of manipulators as example the objec-tive function of layout optimization was built according toeach principle Use GA to generate the solution of layoutscheme which would be transformed into initial pheromoneas ACA required Taking the difference value of comprehen-sive weight of the manipulators for the layout principles asheuristic information ofACA combinedwith the pheromoneprovided by GA the positive feedback optimization mecha-nism of ACA was used to solve further GA-ACA integratedthe complementary advantages of GA and ACA which hadgood optimizing performance and time performance andimproved the design efficiency Taking the 16 manipulators ofZJ1209000DB rig console as layout example layout designsystem of human-computer interaction interface for cabinwas developed by Visual Basic which validated the abovelayout optimization method

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgments

This research was supported by Open Fund (OGE201403-23) of Key Laboratory of Oil amp Gas Equipment Ministryof Education (Southwest Petroleum University) and by theOpen Research Subject (GY-14YB-31) of Key Laboratory(Research Base) of Industrial Design Industry Research Cen-ter Humanities and Social Science EducationDepartment ofSichuan Province

References

[1] J Z Cha X J Tang and Y P Lu ldquoSurvey on packing problemsrdquoJournal of Computer-AidedDesignampComputer Graphics vol 14pp 705ndash711 2002

[2] K A Dowsland and W B Dowsland ldquoPacking problemsrdquoEuropean Journal of Operational Research vol 56 no 1 pp 2ndash14 1992

[3] S Margaritis and NMarmaras ldquoSupporting the design of officelayout meeting ergonomics requirementsrdquo Applied Ergonomicsvol 38 no 6 pp 781ndash790 2007

[4] N Shariatzadeh G Sivard and D Chen ldquoSoftware evalu-ation criteria for rapid factory layout planning design andsimulationrdquo in Proceedings of the 45th CIRP Conference onManufacturing Systems pp 299ndash304 Athens GreeceMay 2012

[5] J Z Huo G Q Li H F Teng and Z G Sun ldquoHuman-computercooperative ant colonygenetic algorithm for satellite modulelayout designrdquo Chinese Journal of Mechanical Engineering vol41 no 3 pp 112ndash116 2005

[6] R Li D M Zhuang R Wang and L R Wang ldquoOptimizationabout the layout work of the steering arrangement in thecockpitrdquo Journal of System Simulation vol 16 pp 1305ndash13072004

[7] S Y Yan Y Chen and L Y Liang ldquoOptimization of controlslayout based on simulated annealing algorithmrdquo Nuclear PowerEngineering vol 35 no 1 pp 67ndash70 2014

[8] L C Zong C Ye S H Yu and D K Chen ldquoResearchand application of intelligent layout method in DSV cabinequipmentrdquo Shipbuilding of China vol 54 no 3 pp 147ndash1542013

[9] L C Zong S H Yu J B Sun L W Han and S S An ldquoStudyon cabin layout optimization with fish algorithmrdquo MechanicalScience and Technology for Aerospace Engineering vol 33 pp257ndash262 2014

[10] Y L Wang C Wang Z S Ji and X G Zhao ldquoA study onintelligent layout design of ship cabinrdquo Shipbuilding of Chinavol 54 pp 139ndash146 2013

[11] D HWolpert andWGMacready ldquoNo free lunch theorems foroptimizationrdquo IEEE Transactions on Evolutionary Computationvol 1 no 1 pp 67ndash82 1997

[12] F Q Zhao G Q Li C Yang A Abraham and H B LiuldquoA human-computer cooperative particle swarm optimizationbased immune algorithm for layout designrdquo Neurocomputingvol 132 pp 68ndash78 2014

[13] A Lodi S Martello and M Monaci ldquoTwo-dimensionalpacking problems a surveyrdquo European Journal of OperationalResearch vol 141 no 2 pp 241ndash252 2002

[14] J R AndersonCognitive Psychology and Itrsquos Implications editedby Y L Qin Posts amp Telecom Press 2012

[15] A Ghanbari S M R Kazemi F Mehmanpazir and M MNakhostin ldquoA cooperative ant colony optimization-geneticalgorithm approach for construction of energy demand fore-casting knowledge-based expert systemsrdquo Knowledge-BasedSystems vol 39 pp 194ndash206 2013

[16] J B Best Cognitive Psychology X T Huang Ed China LightIndustry Press Beijing China 2000

[17] C Wickens J G Hollands S Banbury and R ParasuramanEngineering Psychology amp Human Performance Pearson Lon-don UK 4th edition 2012

[18] L Y Sun Z X Li and T S Jin ldquoCognitive synthetic model andits application in HCIrdquo Journal of Xirsquoan Jiaotong University vol31 pp 74ndash80 1997

[19] Y P Zhang ldquoHuman-computer interface design based onknowledge of cognitive psychologyrdquo Computer Engineering andApplications vol 30 pp 105ndash107 2005

12 Computational Intelligence and Neuroscience

[20] Z Lan ldquoThe cognitive basis of software interface designrdquo ShanxiScience and Technology no 3 pp 41ndash42 1998

[21] X P Wang and L M Cao Genetic AlgorithmmdashTheory Appli-cation and Software Implementation Xirsquoan Jiaotong UniversityPress Xirsquoan China 2002

[22] E Bonabeau M Dorigo and G Theraulaz ldquoInspiration foroptimization from social insect behaviourrdquoNature vol 406 no6791 pp 39ndash42 2000

[23] M Dorigo and L M Gambardella ldquoAnt colonies for thetravelling salesman problemrdquo BioSystems vol 43 no 2 pp 73ndash81 1997

[24] MDorigo VManiezzo andA Colorni ldquoAnt system optimiza-tion by a colony of cooperating agentsrdquo IEEE Transactions onSystems Man and Cybernetics Part B Cybernetics vol 26 no1 pp 29ndash41 1996

[25] J-L Ding Z-Q Chen and Z-Z Yuan ldquoOn the Markovconvergence analysis for the combination of genetic algorithmand ant algorithmrdquo Acta Automatica Sinica vol 30 no 4 pp629ndash634 2004

[26] Z-J Lee S-F Su C-C Chuang and K-H Liu ldquoGeneticalgorithmwith ant colony optimization (GA-ACO) formultiplesequence alignmentrdquo Applied Soft Computing vol 8 no 1 pp55ndash78 2008

[27] Z Li C P Xu W Mo and G J Chen ldquoInitialization forsynchronous sequential circuits based on ant algorithm ampgenetic algorithmrdquo Acta Electronica Sinica vol 31 pp 1276ndash1280 2003

[28] Z-H Xiong S-K Li and J-H Chen ldquoHardwaresoftware par-titioning based on dynamic combination of genetic algorithmand ant algorithmrdquo Journal of Software vol 16 no 4 pp 503ndash512 2005

[29] J-L Ding Z-Q Chen and Z-Z Yuan ldquoOn the combinationof genetic algorithm and ant algorithmrdquo Journal of ComputerResearch and Development vol 40 no 9 pp 1351ndash1356 2003

[30] T Stutzle and H H Hoos ldquoMAX-MIN ant systemrdquo FutureGeneration Computer Systems vol 16 no 8 pp 889ndash914 2000

[31] T Stuetzle and H Hoos ldquoMAX-MIN Ant System and localsearch for the traveling salesman problemrdquo in Proceedings ofthe IEEE International Conference on Evolutionary Computation(ICEC rsquo97) pp 309ndash314 Indianapolis Indiana April 1997

Submit your manuscripts athttpwwwhindawicom

Computer Games Technology

International Journal of

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Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Distributed Sensor Networks

International Journal of

Advances in

FuzzySystems

Hindawi Publishing Corporationhttpwwwhindawicom

Volume 2014

International Journal of

ReconfigurableComputing

Hindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Applied Computational Intelligence and Soft Computing

thinspAdvancesthinspinthinsp

Artificial Intelligence

HindawithinspPublishingthinspCorporationhttpwwwhindawicom Volumethinsp2014

Advances inSoftware EngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Electrical and Computer Engineering

Journal of

Journal of

Computer Networks and Communications

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporation

httpwwwhindawicom Volume 2014

Advances in

Multimedia

International Journal of

Biomedical Imaging

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

ArtificialNeural Systems

Advances in

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

RoboticsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Computational Intelligence and Neuroscience

Industrial EngineeringJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Human-ComputerInteraction

Advances in

Computer EngineeringAdvances in

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Computational Intelligence and Neuroscience 5

SelectCross

Mutate

ttat0

AAGA

N+ 1

N + 1Define the target and the fitness function

Generate a number of optimal solutions

The layout design of human-machineinteraction interface

Output the optimal solution

Update the pheromone of each route

m ants traversal n nodes after the ants pass throughaccording to the optimal route to increase the pheromone

Calculate the probability of each ant moving to the next nodethen according to probability to move the ants to the next node

Initialization parameters take the optimal solution generated by GAas the distribution of initial pheromoneput m ants on n nodes

a

Figure 4 The process of GA-ACA

where 119879119894represents the weight of layout object 119894 relative

to Principle 2 119878119894represents the weight of layout object 119894

relative to Principle 3 119874119894119895represents the correlation between

layout object 119894 and layout object 119895 relative to Principle4 119889119894119895represents the distance between layout object 119894 and

layout object 119895 119899 represents the number of layout objects 119894represents the position number of layout objects 120575

1and 120575

2

represent the position control coefficients 119889119894119896represents the

distance between the layout object 119894 and the best layout point119896 119889119896119898

represents the maximum distance between a layoutpoint and the best layout point 119896 in the whole layout range

25 Establish the Layout Design Model of Human-MachineInteraction Interface Based on GA-ACA

251 GA-ACA In 1975 according to Darwinrsquos theory ofsurvival of the fittest the American scholar John Hollandput forward the genetic algorithm [21] Through parallel andglobal search mode to search the best individual in the opti-mization group GA had good adaptive ability robustnessgenerality and othermerits widely used inmachine learningpattern recognition image processing and other fields

In the early 1990s inspired from antsrsquo foraging behaviorin the nature world the Italian scholar Dorigo et al presentedant colony algorithm [22ndash24] ACA had very strong robust-ness and ability to search a good solution and easy to parallelimplementation Originally it is used to solve the travelingsalesman problem latter widely applied to solve the classicaloptimization problem such as sequential ordering problemand quadratic assignment problem

GA and ACA were stochastic optimization algorithmboth of which had the advantages of global search andrandom search and easily combined with other algorithmsHowever GA had the shortcomings of huge calculation andpoor stability leading to low precision and efficiency ACAneeded long search time and is easily prone to stagnationphenomenonThe combination of GA and ACA could utilizethe advantage of the two algorithms and overcome theirdisadvantages and the research has proved that the hybridalgorithm had good efficiency [25] This new algorithm wasused for multiple sequence alignment [26] initializationfor synchronous sequential circuits [27] hardwaresoftwarepartitioning [28] and other optimization problems

According to the study and experiment of GA and ACA[29] the speed-time curve is shown in Figure 4 In the earlystages of the search (119905

0sim 119905119886) GA has fast convergence When

evolution reaches a certain degree its evolutionary ratewouldfall sharply namely the efficiency is low after 119905

119886 Instead at

the beginning of the search (1199050sim 119905119886) ACA searches slowly

But after the accumulation of pheromone reaches a certainextent the searching activity of ants can present a certainregularity that is to say the speed is rapidly improved after119905119886As shown in Figure 4 this paper put forward combining

the advantages of the two algorithms in layout design GAis adopted in the former process of algorithm (before point119886) and the rapidity randomness and global convergence ofGA are used A number of layout schemes of human-machineinteraction interface are generated as initial solution whichwill be turned into track intensity distribution as initializationpheromone Then ACA is adopted to optimize the layoutschemes in the latter process of algorithm (after point119886) In the case of a certain track intensity distribution ofinitialization pheromone the characteristics of parallelismpositive feedbackmechanism and high solving efficiencywillbe used to improve the efficiency of solving and output theoptimal solution

252 GA to Determine the Initial Pheromone

(1) The Operating Mechanism of GA The process of GA inhuman-machine interaction interface layout is shown at theleft part in Figure 4

(a) Coding Using the form of sequence coding the layoutobjects were expressed by real number to carry out optimiz-ing calculation The code string format shows as follows

119862119898= [119888119898

1 119888119898

2 119888119898

3 119888

119898

119899] (4)

where 119898 represents the population size 119899 represents thenumber of the layout objects 119888119898

119899represents the layout object

in the 119899th position of the119898th chromosomeSuppose there are 10 layout objects which randomly

generated an individual such as [5 4 3 8 7 2 10 1 9 6]Thecode string represents layout object 5 in the first positionlayout object 4 in the second position and so on There are

6 Computational Intelligence and Neuroscience

direct relationships between the objective function and thecoordinate of layout objects If the codes of layout objectsare in different position the corresponding coordinatesare different and the objective function value will also bedifferent

(b) Construct the Fitness Function Take each kind of permu-tation and combination way as an individual and then thebest layout solution was sought by calculating the individualrsquosfitness value and evolutionary operationThe119901th fitness valueof chromosome is as follows

fitness (119901) =119899

sum

119894=1

1205751sdot 119879119894+

119899

sum

119894=1

1205752sdot 119878119894+

119899

sum

119894=1

119899

sum

119895=119894+1

119874119894119895

119889119894119895

(5)

The three parts of fitness function reflect the comprehen-sive situation of layout Principles 2 3 and 4 respectivelythe pros and cons of layout schemes will be judged from theoverall layout

(c) Determine the Evolutionary Mechanism It is done startingfrom the random generation of initial population constantlyrepeating the process of selection crossover and mutationoperation and the population developed along the directionof established goals from generation to generation Accordingto the fitness value of the fitness function excellent indi-viduals would be selected from the current population bywheel bet method so as to form a new population Usingsequence crossover method parent individuals exchangedpart of genes and thus new individuals would be formedUsing swapmutationmethod the diversity of populationwasguaranteed and immature convergence phenomenon wasprevented

(2) The Combination of GA and ACA The combinationopportunity of GA and ACA was dynamically determinedin the operational process of GA First set minimum geneticiterations Genmin andmaximum genetic iterations Genmax inGAThen according to formula (6) record the evolution rateof progeny population in the iterative process and set theminimumevolution rateGen

119901of progeny populationWithin

the scope of the given number of iterations if successiveGen119902generations the evolution rate of progeny population

were less than Gen119901 this showed that the optimization speed

was very low The process of GA should be terminated andturned into the ACA The 10 of the optimal individuals inlast generation in GA would be selected as the carrier whichwould be taken as the suboptimal solution to distribute theinitial pheromone in ACA and optimal solution would beobtained further

EV119896 =sum119898

119894=1(119891119896

119894minus 119891119896minus1

119894) 119891119896minus1

119894

119898 119896 = 2 3 119899 (6)

where 119898 represents population size 119896 represents evolutionalgeneration 119891119896

119894represents the fitness value of individual 119894 in

119896th iteration

253 ACA to Solve Pareto Solution The purpose of layoutoptimization of human-machine interaction interface was to

get an optimal solution to satisfy the design goal which wasto find an optimal path from layout object 119894 to layout object119895 and the optimized goal was to get a maximum of (1) Inthe ACA system model each feasible solution represented apath that an ant walked by It is a shortest path problem sothe objective function of ACA was defined as the reciprocalof the objective function of GA

119871119896=

1

119891 (119894)best (7)

(1) State Transition Rules The process of ACA in human-machine interaction interface layout is shown at the right partin Figure 4 Take the layout of manipulators to describe therealization process of ACA [30 31] Set 119887

119894(119905) (119894 = 1 2 119899)

represents the number of ants of manipulator 119894 in time 119905119898 =

sum119899

119894=1119887119894(119905) represents the total number of ants and 119899 represents

the number of manipulators Set tabu119896represents the tabu

table of ant 119896 which will be adjusted dynamically along withthe ant optimization process In initial stage 119898 ants willbe placed on 119899 manipulators randomly The first elementof each antrsquos tabu table will be set as its first manipulatorIn the process of each iteration each ant chooses the nextmanipulator by the state transition rules repeatedly Afterselection for 119899minus1 times a group ofmanipulators layoutwill begenerated eventually The probability of ant 119896 transfers frommanipulator 119894 to manipulator 119895 in time 119905 is as follows

119901119896

119894119895(119905) =

[120591119894119895(119905)]120572

[120578119894119895]120573

sum119897isin119880

[120591119894119897(119905)]120572

[120578119894119897]120573

119895 isin 119880

0 119895 notin 119880

120578119894119895=

1

V119894119895

V119894119895=10038161003816100381610038161003816119879119894minus 119879119895

10038161003816100381610038161003816+10038161003816100381610038161003816119878119894minus 119878119895

10038161003816100381610038161003816+10038161003816100381610038161003816119874119894minus 119874119895

10038161003816100381610038161003816

(8)

where 120591119894119895(119905) represents the pheromone between manipulator

119894 and manipulator 119895 at 119905 moment 120578119894119895represents the visibility

(heuristic information) of transferring from manipulator 119894to manipulator 119895 V

119894119895represents the difference value of com-

prehensive weight between manipulator 119894 and manipulator119895 to the layout principles 120572 (120572 ge 0) represents the relativeimportance of 120591 120573 (120573 ge 0) represents the relative importanceof 120578 119880 represents the feasible point set namely the set ofmanipulators can be chosen by ant 119896 at 119905moment

(2) Pheromone andHeuristic InformationThis paper adoptedthe max-min ant system (MMAS) which was proposed bythe Belgium scholar Thomas Due to the combined withGA initialized pheromone was different fromMMAS At theinitial moment initial pheromone value is set as follows

120591119894119895(0) = 120591

119862+ 120591119866 (9)

where 120591119862is the given pheromone constant based on the

specific scale of solution equivalent to the 120591min in MMAS120591119866represents the value of pheromone converted from the

solution of GA

Computational Intelligence and Neuroscience 7

Table 1 Partition coding for manipulators

The name of the manipulator Code The name of the manipulator Code

Range 1

E-BRAKE 1

Range 3

LEFT CATHEAD 9GEN E-STOP 2 RIGHT CATHEAD 10VFD E-STOP 3 ROTARY CATHEAD 11

PARKING BRAKE 4 ALARM 12

Range 2

RT MOTOR CONTROL 5

Range 4

AIR SPINNER 13RT INERTIA BRAKE 6 TOOLS SELECT 14RT SPEED SETTING 7 SPARE1 15RT TORQUE LIMIT 8 SPARE2 16

Ant chose next manipulator mainly according topheromone 120591

119894119895(119905) and heuristic information 120578

119894119895 120578119894119895

wasgiven by the layout problem to be solved and affected by V

119894119895

which remained unchanged during the operation processin algorithm Ants released pheromone on the path theypassed by In order to avoid too much residual informationto submerge heuristic information residual informationshould be upgraded After ants traversed through all themanipulators the pheromone updated in the environmentThe update equation is as follows

120591119894119895(119905 + 1) = (1 minus 120588) sdot 120591

119894119895(119905) + Δ120591

119894119895(119905)

Δ120591119894119895(119905) = sumΔ120591

119896

119894119895(119905)

Δ120591119896

119894119895(119905) =

119876

119871119896

(ant 119896 passes though (119894 119895))

0 (otherwise)

(10)

where 120588 (0 le 120588 le 1) represents the volatilization coefficientof pheromone Δ120591119896

119894119895(119905) represents the amount of pheromone

released between manipulator 119894 and manipulator 119895 by ant 119896in this cycle Δ120591

119894119895(119905) represents the increment of pheromone

between manipulator 119894 and manipulator 119895 after this cycle 119876is a constant which represents the amount of pheromone 119871

119896

represents the objective function value corresponding withthe layout of all the manipulators formed by ant 119896 traversedthrough in this cycle

Only the ant with optimal layout scheme could updatepheromone in one cycle The amount of pheromone on eachpath would be limited in scope of [120591min 120591max] If beyondthis range the pheromone would be mandatory set as 120591minor 120591max Compared with the standard ACA the improvedalgorithm prevented premature stagnation With all theants completing the traverse of all the manipulators thepheromone accumulated and volatilized continuously Untilreaching the number of iterations or a certain fitness valuethe optimal path formed by ants was the best layout scheme

3 Application Verification

31 Layout Design of Driller Control Room on Drilling RigTake the layout design of human-machine interaction inter-face of driller control room on drilling rig as an exampleto illustrate the proposed method The 16 manipulators of

Table 2 The size of each type of manipulators (mm)

The types of manipulators Size The codes of manipulatorsSwitch knobs Φ30 4 5 6 9 10 11 12 13 14 15 16Rotary knobs Φ50 7 8Buttons Φ40 1 2 3

ZJ1209000DB rig console would be arranged (the grey areaas shown in Figure 5) First of all the 16 manipulators needto be encoded As shown in Table 1 when encoding layoutPrinciple 1 needs to be reflected that is manipulators withsimilar function will be arranged in one area to reduce thesearch time for operators

It is necessary to simplify the layout area and layoutobjects when describing themathematical model to facilitatea digital description of design variables According to thehuman upper limb dimension to determine the layout spaceand simplify the layout area as rectangular area (450 times

400mm) there are three types of manipulators includingbuttons rotary knobs and switch knobs Their sizes areslightly different and the specific sizes are shown in Table 2The spaces between manipulators are set as equal and theinterval is 100mm After being simplified the layout ofmanipulators can be regarded as a scheduling problem firstusing GA-ACA to find a good sorting and then according tothe order to establish the actual layout

Drilling process is mainly divided into three workingconditions pull out of hole run in hole and normal drillingConsidering the principle of operating sequence in multipleworking conditions determine the manipulatorsrsquo operatingsequence and calculate each manipulatorrsquos weight relativeto Principle 2 Through questionnaires to consult drillersdetermine the relative judgment matrix of the manipulatorsrsquoimportance and frequency and calculate each manipulatorrsquosweight relative to Principle 3 Analytic hierarchy process willbe used to calculate each manipulatorrsquos weight of 119879

119894and

119878119894 and the calculation result is shown in Table 3 Finally

determine the relative correlation between the manipulatorsThe correlation between manipulator and itself is expressedwith 1 and the correlation between themanipulator and othermanipulators is expressed in decimal within 0sim1 The greaterthe correlation between manipulators the greater the corre-lation value The correlation between the 16 manipulators isshown in Table 4

8 Computational Intelligence and Neuroscience

Table 3 The manipulatorsrsquo weight relative to Principles 2 and 3

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16119879119894

0018 0018 0018 0055 0113 0076 0205 0163 0068 0068 0068 0016 0016 0098 0 0119878119894

0163 0115 0115 0115 0069 0069 0069 0069 0043 0043 0043 0013 0026 0026 0011 0011

LEFT DRUMSHIFT LOCK

AIR SLIPS

LEFT DRUMCLUTCH

AIR HORN

AD MODE AD MOTOR

LEFT AD LEFT ADCLUTCH

TOP SCREENWIPER CONTROL SPEED SETTING

PROTECTION RESET

SILIENCERESET

DW SPEEDCONTROL

KEYBOARD12 13 3 2 9

101 4

5 7

6 8

11 14

15 16

PARKINGBRAKE

RT INERTIABRAKE

RT SPEEDSETTING

TOOLSSELECT

E-BRAKE

VFDE-STOP E-STOPGEN

RT MOTORCONTROL

RT TORQUELIMIT

LEFT

RIGHTCATHEAD

CATHEAD

ROTARYCATHEAD

ALARM

AIRSPINNER

SPARE2SPARE1

Armrest

WORKINGBRAKERIGHT DRUM

SHIFT LOCKRIGHT DRUMCLUTCH

RIGHT ADSHIFT LOCH

SHIFT LOCH

RIGHT ADCLUTCH

FRONT SCREENWIPER CONTROL

Number 2 PUMP

Number 1 PUMP Number 3 PUMP

SPEED SETTING

SPEED SETTING

Figure 5 Layout schematic diagram of ZJ1209000DB rig console (before optimization)

32 Optimization Result After completing the above datapreparation as shown in Figure 6 the operating parametersof GA and ACA need to be set up before the computeraided optimization calculation proceeds First set the controlparameters of GA population size 119873 = 50 crossover rate119901119888= 06 mutation rate 119901

119898= 02 and number of partition

119904 = 4 Second set the control parameters ofACAThe amountof ants is119898 = 10 120572 = 1 120573 = 1 and 120588 = 05

In the system of GA-ACA calculation module set theend condition of GA minimum genetic iterations Genmin =

15 maximum genetic iterations Genmax = 50 minimumevolution rate Gene

119901= 3 and Gene

119902= 3 Set the control

parameters of pheromone 119876 = 500 120591min = 10 and 120591max =

100 The optimal 10 of individuals in the last generationin GA would be selected as the solution set of geneticoptimization which would be transformed into pheromone

values If manipulator 119894 was adjacent to manipulator 119895 ingenetic optimization solution the pheromone added 10 onthe path (119894 119895) and all the initial values of pheromonesshould be set like this When meeting one of the followingconditions theACA should stop (1)Thenumber of iterationsreaches antmax = 50 (2) For continuous three generations inthe iteration the improvement rate of offspring optimizationis less than 05

Click the button of algorithm running on the systeminterface the system calculates via the program after 50times genetic iterations and 11 times ant colony optimizationiteration the value of the optimal layout scheme is 2822The sequence of the manipulators is obtained that is 4 13 2 6 5 7 8 9 10 12 11 14 13 15 and 16 Accordingto the optimized sequence the layout scheme is shown inFigure 7

Computational Intelligence and Neuroscience 9

Table 4 The correlation between the 16 manipulators

119874119894119895

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 161 1 05 07 09 0 0 0 0 0 0 0 0 0 0 0 02 05 1 09 05 0 0 0 0 0 0 0 0 0 0 0 03 07 09 1 07 0 0 0 0 0 0 0 0 0 0 0 04 09 05 07 1 0 0 0 0 0 0 0 0 0 0 0 05 0 0 0 0 1 09 07 09 0 0 0 0 0 0 0 06 0 0 0 0 09 1 05 07 0 0 0 0 0 0 0 07 0 0 0 0 07 05 1 09 0 0 0 0 0 0 0 08 0 0 0 0 09 07 09 1 0 0 0 0 0 0 0 09 0 0 0 0 0 0 0 0 1 09 07 05 0 0 0 010 0 0 0 0 0 0 0 0 09 1 07 05 0 0 0 011 0 0 0 0 0 0 0 0 07 07 1 05 0 0 0 012 0 0 0 0 0 0 0 0 05 05 05 1 0 0 0 013 0 0 0 0 0 0 0 0 0 0 0 0 1 09 03 0314 0 0 0 0 0 0 0 0 0 0 0 0 09 1 03 0315 0 0 0 0 0 0 0 0 0 0 0 0 03 03 1 0916 0 0 0 0 0 0 0 0 0 0 0 0 03 03 09 1

Table 5 The comparison between GA ACA and ACA

Algorithm Layout scheme The value of layout scheme Number of iterationsGA 4 2 3 1 7 8 6 5 12 11 9 10 13 14 15 16 2814 82ACA 4 1 2 3 5 6 7 8 9 10 11 12 15 16 14 13 2773 75GA-ACA 4 1 2 3 7 8 5 6 12 9 10 11 15 16 13 14 2835 50 + 13

Figure 6 System interface of layout design

Because the proposed method is still in the research anddevelopment there is certain difference between the solutionand the actual situation Besides the human cognitive activ-ities and layout experience cannot be completely describedby the layout model layout scheme obtained by algorithmoptimization is close to the optimal solution rather than theoptimal solution In this stage the designer needs to adjustthe layout scheme according to the actual needs Similarlysort the manipulators on the console at the left hand Forthe sake of evaluating layout scheme intuitively computersimulation design modeling is proceeded Through detaileddesign the final layout scheme is shown in Figure 8

The CATIA software will be used to evaluate the resultof the layout CATIA V5 integrates four ergonomic moduleswhich can evaluate visibility accessibility the comfort ofposture (analyze the angle of each joint) and so on The bluearea in Figure 9 shows the reach envelope of drillerrsquos righthand Under natural state the drillerrsquos arm can operate themain manipulators on console This shows that the locationsof manipulators were in the human bodyrsquos correspondingrange of joint motion and in accord with the armrsquos motiontrail In Figure 10 the virtual human model simulates oneoperating posture of driller And the postural score analysisin Figure 11 shows the comfort score of lumbar vertebrathoracic vertebra head arm and forearmThe sores indicatethat this operating posture is comfortable the position of thismanipulator matches with the movement characteristics ofthe human body

33 Algorithm Comparison Using single GA ACA and GA-ACA hybrid algorithm to solve the above layout optimizationproblem respectively set the number of test times as 10each Result comparison is shown in Table 5 the columnof layout scheme shows the optimal result in 10 timesrsquorunning and the third and the fourth columns show theaverage value of layout scheme and the average number ofiterations As can be seen from the test result no matterthe solution speed or precision the GA-ACA is superiorto single GA and ACA in solving layout optimizationproblem

10 Computational Intelligence and Neuroscience

GEN E-STOPVFDE-STOP

LEFT RIGHT

E-BRAKEPARKINGBRAKE

RT INERTIABRAKE

RT SPEEDSETTING LIMIT

RT MOTORCONTROL

RT TORQUE ROTARYCATHEAD

CATHEAD CATHEAD

AIR SPARE1 SPARE2

ALARM

TOOLSSELECT

WORKINGBRAKE

Armrest0

200

0 200 400 600 800 10002004006008001000

400

600

800

Comfortableoperating range

operating rangeMaximum

1 3 24

56

7 8

9 10

1112

1314 15 16

SPINNER

Range 1

Range 2 Range 3

Range 4

y

x

Figure 7 Layout scheme of right console (after optimization)

Figure 8 Modeling and simulation of layout scheme

Figure 9 Analyze the right handrsquos reach envelope

34 Discussion Through reference to previous researchabout cognitive model this paper integrated their advantagesand put forward the cognitive model of human-machineinteraction interface suitable for cabin This model describedthe cognitive process of operators working in the cabinAnd then by analyzing the information processing processthe layout principles of human-machine interaction interfacewere summed up which would be quantified in the objectivefunction However human cognitive behavior process is verycomplicated part of the cognitive activities is difficult toexpress by formalized methods layout principles needed tobe improved further There were lots of influence factors in

Figure 10 The simulation of operating posture

Figure 11 The comfort evaluation of operating posture

actual engineering problem the layout problem was difficultto be described completely by mathematical model

It has become common that the bionic intelligencealgorithms were applied in the layout optimization designbut cognitive characteristics were seldom considered as theconstraints of layout optimization algorithm FurthermoreGA-ACA combined the advantages of two algorithms whichwould be more precise to solve layout optimization problemThe layout design of human-machine interaction interfaceof driller control room on drilling rig has illustrated theproposed method According to the characteristics of thehuman-machine interaction interface of different product

Computational Intelligence and Neuroscience 11

this method could be modified and adapted As a layoutergonomic design method it could assist designers andengineers to conduct human-machine interaction interfacedesign and improve the efficiency in layout design

4 Conclusion

Based on the theory of cognitive psychology according toWickens information processing model the cognitive modelof human-machine interaction interface was establishedConsidering peoplersquos information organization law visualsearch law user memory characteristics and so on thehuman-machine interaction interface should conform tooperatorsrsquo cognitive ability for interface information Basedon the cognitive model the layout principles of human-machine interaction interface were summarized as the layoutconstraintsThe problem of solving layout scheme was trans-formed into combinatorial optimization problem and GA-ACA was put forward to solve the problem which realizedthe algorithmization of artificial optimization process

Taking the layout of manipulators as example the objec-tive function of layout optimization was built according toeach principle Use GA to generate the solution of layoutscheme which would be transformed into initial pheromoneas ACA required Taking the difference value of comprehen-sive weight of the manipulators for the layout principles asheuristic information ofACA combinedwith the pheromoneprovided by GA the positive feedback optimization mecha-nism of ACA was used to solve further GA-ACA integratedthe complementary advantages of GA and ACA which hadgood optimizing performance and time performance andimproved the design efficiency Taking the 16 manipulators ofZJ1209000DB rig console as layout example layout designsystem of human-computer interaction interface for cabinwas developed by Visual Basic which validated the abovelayout optimization method

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgments

This research was supported by Open Fund (OGE201403-23) of Key Laboratory of Oil amp Gas Equipment Ministryof Education (Southwest Petroleum University) and by theOpen Research Subject (GY-14YB-31) of Key Laboratory(Research Base) of Industrial Design Industry Research Cen-ter Humanities and Social Science EducationDepartment ofSichuan Province

References

[1] J Z Cha X J Tang and Y P Lu ldquoSurvey on packing problemsrdquoJournal of Computer-AidedDesignampComputer Graphics vol 14pp 705ndash711 2002

[2] K A Dowsland and W B Dowsland ldquoPacking problemsrdquoEuropean Journal of Operational Research vol 56 no 1 pp 2ndash14 1992

[3] S Margaritis and NMarmaras ldquoSupporting the design of officelayout meeting ergonomics requirementsrdquo Applied Ergonomicsvol 38 no 6 pp 781ndash790 2007

[4] N Shariatzadeh G Sivard and D Chen ldquoSoftware evalu-ation criteria for rapid factory layout planning design andsimulationrdquo in Proceedings of the 45th CIRP Conference onManufacturing Systems pp 299ndash304 Athens GreeceMay 2012

[5] J Z Huo G Q Li H F Teng and Z G Sun ldquoHuman-computercooperative ant colonygenetic algorithm for satellite modulelayout designrdquo Chinese Journal of Mechanical Engineering vol41 no 3 pp 112ndash116 2005

[6] R Li D M Zhuang R Wang and L R Wang ldquoOptimizationabout the layout work of the steering arrangement in thecockpitrdquo Journal of System Simulation vol 16 pp 1305ndash13072004

[7] S Y Yan Y Chen and L Y Liang ldquoOptimization of controlslayout based on simulated annealing algorithmrdquo Nuclear PowerEngineering vol 35 no 1 pp 67ndash70 2014

[8] L C Zong C Ye S H Yu and D K Chen ldquoResearchand application of intelligent layout method in DSV cabinequipmentrdquo Shipbuilding of China vol 54 no 3 pp 147ndash1542013

[9] L C Zong S H Yu J B Sun L W Han and S S An ldquoStudyon cabin layout optimization with fish algorithmrdquo MechanicalScience and Technology for Aerospace Engineering vol 33 pp257ndash262 2014

[10] Y L Wang C Wang Z S Ji and X G Zhao ldquoA study onintelligent layout design of ship cabinrdquo Shipbuilding of Chinavol 54 pp 139ndash146 2013

[11] D HWolpert andWGMacready ldquoNo free lunch theorems foroptimizationrdquo IEEE Transactions on Evolutionary Computationvol 1 no 1 pp 67ndash82 1997

[12] F Q Zhao G Q Li C Yang A Abraham and H B LiuldquoA human-computer cooperative particle swarm optimizationbased immune algorithm for layout designrdquo Neurocomputingvol 132 pp 68ndash78 2014

[13] A Lodi S Martello and M Monaci ldquoTwo-dimensionalpacking problems a surveyrdquo European Journal of OperationalResearch vol 141 no 2 pp 241ndash252 2002

[14] J R AndersonCognitive Psychology and Itrsquos Implications editedby Y L Qin Posts amp Telecom Press 2012

[15] A Ghanbari S M R Kazemi F Mehmanpazir and M MNakhostin ldquoA cooperative ant colony optimization-geneticalgorithm approach for construction of energy demand fore-casting knowledge-based expert systemsrdquo Knowledge-BasedSystems vol 39 pp 194ndash206 2013

[16] J B Best Cognitive Psychology X T Huang Ed China LightIndustry Press Beijing China 2000

[17] C Wickens J G Hollands S Banbury and R ParasuramanEngineering Psychology amp Human Performance Pearson Lon-don UK 4th edition 2012

[18] L Y Sun Z X Li and T S Jin ldquoCognitive synthetic model andits application in HCIrdquo Journal of Xirsquoan Jiaotong University vol31 pp 74ndash80 1997

[19] Y P Zhang ldquoHuman-computer interface design based onknowledge of cognitive psychologyrdquo Computer Engineering andApplications vol 30 pp 105ndash107 2005

12 Computational Intelligence and Neuroscience

[20] Z Lan ldquoThe cognitive basis of software interface designrdquo ShanxiScience and Technology no 3 pp 41ndash42 1998

[21] X P Wang and L M Cao Genetic AlgorithmmdashTheory Appli-cation and Software Implementation Xirsquoan Jiaotong UniversityPress Xirsquoan China 2002

[22] E Bonabeau M Dorigo and G Theraulaz ldquoInspiration foroptimization from social insect behaviourrdquoNature vol 406 no6791 pp 39ndash42 2000

[23] M Dorigo and L M Gambardella ldquoAnt colonies for thetravelling salesman problemrdquo BioSystems vol 43 no 2 pp 73ndash81 1997

[24] MDorigo VManiezzo andA Colorni ldquoAnt system optimiza-tion by a colony of cooperating agentsrdquo IEEE Transactions onSystems Man and Cybernetics Part B Cybernetics vol 26 no1 pp 29ndash41 1996

[25] J-L Ding Z-Q Chen and Z-Z Yuan ldquoOn the Markovconvergence analysis for the combination of genetic algorithmand ant algorithmrdquo Acta Automatica Sinica vol 30 no 4 pp629ndash634 2004

[26] Z-J Lee S-F Su C-C Chuang and K-H Liu ldquoGeneticalgorithmwith ant colony optimization (GA-ACO) formultiplesequence alignmentrdquo Applied Soft Computing vol 8 no 1 pp55ndash78 2008

[27] Z Li C P Xu W Mo and G J Chen ldquoInitialization forsynchronous sequential circuits based on ant algorithm ampgenetic algorithmrdquo Acta Electronica Sinica vol 31 pp 1276ndash1280 2003

[28] Z-H Xiong S-K Li and J-H Chen ldquoHardwaresoftware par-titioning based on dynamic combination of genetic algorithmand ant algorithmrdquo Journal of Software vol 16 no 4 pp 503ndash512 2005

[29] J-L Ding Z-Q Chen and Z-Z Yuan ldquoOn the combinationof genetic algorithm and ant algorithmrdquo Journal of ComputerResearch and Development vol 40 no 9 pp 1351ndash1356 2003

[30] T Stutzle and H H Hoos ldquoMAX-MIN ant systemrdquo FutureGeneration Computer Systems vol 16 no 8 pp 889ndash914 2000

[31] T Stuetzle and H Hoos ldquoMAX-MIN Ant System and localsearch for the traveling salesman problemrdquo in Proceedings ofthe IEEE International Conference on Evolutionary Computation(ICEC rsquo97) pp 309ndash314 Indianapolis Indiana April 1997

Submit your manuscripts athttpwwwhindawicom

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Distributed Sensor Networks

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Hindawi Publishing Corporationhttpwwwhindawicom

Volume 2014

International Journal of

ReconfigurableComputing

Hindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Applied Computational Intelligence and Soft Computing

thinspAdvancesthinspinthinsp

Artificial Intelligence

HindawithinspPublishingthinspCorporationhttpwwwhindawicom Volumethinsp2014

Advances inSoftware EngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Electrical and Computer Engineering

Journal of

Journal of

Computer Networks and Communications

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporation

httpwwwhindawicom Volume 2014

Advances in

Multimedia

International Journal of

Biomedical Imaging

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

ArtificialNeural Systems

Advances in

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

RoboticsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Computational Intelligence and Neuroscience

Industrial EngineeringJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Human-ComputerInteraction

Advances in

Computer EngineeringAdvances in

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

6 Computational Intelligence and Neuroscience

direct relationships between the objective function and thecoordinate of layout objects If the codes of layout objectsare in different position the corresponding coordinatesare different and the objective function value will also bedifferent

(b) Construct the Fitness Function Take each kind of permu-tation and combination way as an individual and then thebest layout solution was sought by calculating the individualrsquosfitness value and evolutionary operationThe119901th fitness valueof chromosome is as follows

fitness (119901) =119899

sum

119894=1

1205751sdot 119879119894+

119899

sum

119894=1

1205752sdot 119878119894+

119899

sum

119894=1

119899

sum

119895=119894+1

119874119894119895

119889119894119895

(5)

The three parts of fitness function reflect the comprehen-sive situation of layout Principles 2 3 and 4 respectivelythe pros and cons of layout schemes will be judged from theoverall layout

(c) Determine the Evolutionary Mechanism It is done startingfrom the random generation of initial population constantlyrepeating the process of selection crossover and mutationoperation and the population developed along the directionof established goals from generation to generation Accordingto the fitness value of the fitness function excellent indi-viduals would be selected from the current population bywheel bet method so as to form a new population Usingsequence crossover method parent individuals exchangedpart of genes and thus new individuals would be formedUsing swapmutationmethod the diversity of populationwasguaranteed and immature convergence phenomenon wasprevented

(2) The Combination of GA and ACA The combinationopportunity of GA and ACA was dynamically determinedin the operational process of GA First set minimum geneticiterations Genmin andmaximum genetic iterations Genmax inGAThen according to formula (6) record the evolution rateof progeny population in the iterative process and set theminimumevolution rateGen

119901of progeny populationWithin

the scope of the given number of iterations if successiveGen119902generations the evolution rate of progeny population

were less than Gen119901 this showed that the optimization speed

was very low The process of GA should be terminated andturned into the ACA The 10 of the optimal individuals inlast generation in GA would be selected as the carrier whichwould be taken as the suboptimal solution to distribute theinitial pheromone in ACA and optimal solution would beobtained further

EV119896 =sum119898

119894=1(119891119896

119894minus 119891119896minus1

119894) 119891119896minus1

119894

119898 119896 = 2 3 119899 (6)

where 119898 represents population size 119896 represents evolutionalgeneration 119891119896

119894represents the fitness value of individual 119894 in

119896th iteration

253 ACA to Solve Pareto Solution The purpose of layoutoptimization of human-machine interaction interface was to

get an optimal solution to satisfy the design goal which wasto find an optimal path from layout object 119894 to layout object119895 and the optimized goal was to get a maximum of (1) Inthe ACA system model each feasible solution represented apath that an ant walked by It is a shortest path problem sothe objective function of ACA was defined as the reciprocalof the objective function of GA

119871119896=

1

119891 (119894)best (7)

(1) State Transition Rules The process of ACA in human-machine interaction interface layout is shown at the right partin Figure 4 Take the layout of manipulators to describe therealization process of ACA [30 31] Set 119887

119894(119905) (119894 = 1 2 119899)

represents the number of ants of manipulator 119894 in time 119905119898 =

sum119899

119894=1119887119894(119905) represents the total number of ants and 119899 represents

the number of manipulators Set tabu119896represents the tabu

table of ant 119896 which will be adjusted dynamically along withthe ant optimization process In initial stage 119898 ants willbe placed on 119899 manipulators randomly The first elementof each antrsquos tabu table will be set as its first manipulatorIn the process of each iteration each ant chooses the nextmanipulator by the state transition rules repeatedly Afterselection for 119899minus1 times a group ofmanipulators layoutwill begenerated eventually The probability of ant 119896 transfers frommanipulator 119894 to manipulator 119895 in time 119905 is as follows

119901119896

119894119895(119905) =

[120591119894119895(119905)]120572

[120578119894119895]120573

sum119897isin119880

[120591119894119897(119905)]120572

[120578119894119897]120573

119895 isin 119880

0 119895 notin 119880

120578119894119895=

1

V119894119895

V119894119895=10038161003816100381610038161003816119879119894minus 119879119895

10038161003816100381610038161003816+10038161003816100381610038161003816119878119894minus 119878119895

10038161003816100381610038161003816+10038161003816100381610038161003816119874119894minus 119874119895

10038161003816100381610038161003816

(8)

where 120591119894119895(119905) represents the pheromone between manipulator

119894 and manipulator 119895 at 119905 moment 120578119894119895represents the visibility

(heuristic information) of transferring from manipulator 119894to manipulator 119895 V

119894119895represents the difference value of com-

prehensive weight between manipulator 119894 and manipulator119895 to the layout principles 120572 (120572 ge 0) represents the relativeimportance of 120591 120573 (120573 ge 0) represents the relative importanceof 120578 119880 represents the feasible point set namely the set ofmanipulators can be chosen by ant 119896 at 119905moment

(2) Pheromone andHeuristic InformationThis paper adoptedthe max-min ant system (MMAS) which was proposed bythe Belgium scholar Thomas Due to the combined withGA initialized pheromone was different fromMMAS At theinitial moment initial pheromone value is set as follows

120591119894119895(0) = 120591

119862+ 120591119866 (9)

where 120591119862is the given pheromone constant based on the

specific scale of solution equivalent to the 120591min in MMAS120591119866represents the value of pheromone converted from the

solution of GA

Computational Intelligence and Neuroscience 7

Table 1 Partition coding for manipulators

The name of the manipulator Code The name of the manipulator Code

Range 1

E-BRAKE 1

Range 3

LEFT CATHEAD 9GEN E-STOP 2 RIGHT CATHEAD 10VFD E-STOP 3 ROTARY CATHEAD 11

PARKING BRAKE 4 ALARM 12

Range 2

RT MOTOR CONTROL 5

Range 4

AIR SPINNER 13RT INERTIA BRAKE 6 TOOLS SELECT 14RT SPEED SETTING 7 SPARE1 15RT TORQUE LIMIT 8 SPARE2 16

Ant chose next manipulator mainly according topheromone 120591

119894119895(119905) and heuristic information 120578

119894119895 120578119894119895

wasgiven by the layout problem to be solved and affected by V

119894119895

which remained unchanged during the operation processin algorithm Ants released pheromone on the path theypassed by In order to avoid too much residual informationto submerge heuristic information residual informationshould be upgraded After ants traversed through all themanipulators the pheromone updated in the environmentThe update equation is as follows

120591119894119895(119905 + 1) = (1 minus 120588) sdot 120591

119894119895(119905) + Δ120591

119894119895(119905)

Δ120591119894119895(119905) = sumΔ120591

119896

119894119895(119905)

Δ120591119896

119894119895(119905) =

119876

119871119896

(ant 119896 passes though (119894 119895))

0 (otherwise)

(10)

where 120588 (0 le 120588 le 1) represents the volatilization coefficientof pheromone Δ120591119896

119894119895(119905) represents the amount of pheromone

released between manipulator 119894 and manipulator 119895 by ant 119896in this cycle Δ120591

119894119895(119905) represents the increment of pheromone

between manipulator 119894 and manipulator 119895 after this cycle 119876is a constant which represents the amount of pheromone 119871

119896

represents the objective function value corresponding withthe layout of all the manipulators formed by ant 119896 traversedthrough in this cycle

Only the ant with optimal layout scheme could updatepheromone in one cycle The amount of pheromone on eachpath would be limited in scope of [120591min 120591max] If beyondthis range the pheromone would be mandatory set as 120591minor 120591max Compared with the standard ACA the improvedalgorithm prevented premature stagnation With all theants completing the traverse of all the manipulators thepheromone accumulated and volatilized continuously Untilreaching the number of iterations or a certain fitness valuethe optimal path formed by ants was the best layout scheme

3 Application Verification

31 Layout Design of Driller Control Room on Drilling RigTake the layout design of human-machine interaction inter-face of driller control room on drilling rig as an exampleto illustrate the proposed method The 16 manipulators of

Table 2 The size of each type of manipulators (mm)

The types of manipulators Size The codes of manipulatorsSwitch knobs Φ30 4 5 6 9 10 11 12 13 14 15 16Rotary knobs Φ50 7 8Buttons Φ40 1 2 3

ZJ1209000DB rig console would be arranged (the grey areaas shown in Figure 5) First of all the 16 manipulators needto be encoded As shown in Table 1 when encoding layoutPrinciple 1 needs to be reflected that is manipulators withsimilar function will be arranged in one area to reduce thesearch time for operators

It is necessary to simplify the layout area and layoutobjects when describing themathematical model to facilitatea digital description of design variables According to thehuman upper limb dimension to determine the layout spaceand simplify the layout area as rectangular area (450 times

400mm) there are three types of manipulators includingbuttons rotary knobs and switch knobs Their sizes areslightly different and the specific sizes are shown in Table 2The spaces between manipulators are set as equal and theinterval is 100mm After being simplified the layout ofmanipulators can be regarded as a scheduling problem firstusing GA-ACA to find a good sorting and then according tothe order to establish the actual layout

Drilling process is mainly divided into three workingconditions pull out of hole run in hole and normal drillingConsidering the principle of operating sequence in multipleworking conditions determine the manipulatorsrsquo operatingsequence and calculate each manipulatorrsquos weight relativeto Principle 2 Through questionnaires to consult drillersdetermine the relative judgment matrix of the manipulatorsrsquoimportance and frequency and calculate each manipulatorrsquosweight relative to Principle 3 Analytic hierarchy process willbe used to calculate each manipulatorrsquos weight of 119879

119894and

119878119894 and the calculation result is shown in Table 3 Finally

determine the relative correlation between the manipulatorsThe correlation between manipulator and itself is expressedwith 1 and the correlation between themanipulator and othermanipulators is expressed in decimal within 0sim1 The greaterthe correlation between manipulators the greater the corre-lation value The correlation between the 16 manipulators isshown in Table 4

8 Computational Intelligence and Neuroscience

Table 3 The manipulatorsrsquo weight relative to Principles 2 and 3

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16119879119894

0018 0018 0018 0055 0113 0076 0205 0163 0068 0068 0068 0016 0016 0098 0 0119878119894

0163 0115 0115 0115 0069 0069 0069 0069 0043 0043 0043 0013 0026 0026 0011 0011

LEFT DRUMSHIFT LOCK

AIR SLIPS

LEFT DRUMCLUTCH

AIR HORN

AD MODE AD MOTOR

LEFT AD LEFT ADCLUTCH

TOP SCREENWIPER CONTROL SPEED SETTING

PROTECTION RESET

SILIENCERESET

DW SPEEDCONTROL

KEYBOARD12 13 3 2 9

101 4

5 7

6 8

11 14

15 16

PARKINGBRAKE

RT INERTIABRAKE

RT SPEEDSETTING

TOOLSSELECT

E-BRAKE

VFDE-STOP E-STOPGEN

RT MOTORCONTROL

RT TORQUELIMIT

LEFT

RIGHTCATHEAD

CATHEAD

ROTARYCATHEAD

ALARM

AIRSPINNER

SPARE2SPARE1

Armrest

WORKINGBRAKERIGHT DRUM

SHIFT LOCKRIGHT DRUMCLUTCH

RIGHT ADSHIFT LOCH

SHIFT LOCH

RIGHT ADCLUTCH

FRONT SCREENWIPER CONTROL

Number 2 PUMP

Number 1 PUMP Number 3 PUMP

SPEED SETTING

SPEED SETTING

Figure 5 Layout schematic diagram of ZJ1209000DB rig console (before optimization)

32 Optimization Result After completing the above datapreparation as shown in Figure 6 the operating parametersof GA and ACA need to be set up before the computeraided optimization calculation proceeds First set the controlparameters of GA population size 119873 = 50 crossover rate119901119888= 06 mutation rate 119901

119898= 02 and number of partition

119904 = 4 Second set the control parameters ofACAThe amountof ants is119898 = 10 120572 = 1 120573 = 1 and 120588 = 05

In the system of GA-ACA calculation module set theend condition of GA minimum genetic iterations Genmin =

15 maximum genetic iterations Genmax = 50 minimumevolution rate Gene

119901= 3 and Gene

119902= 3 Set the control

parameters of pheromone 119876 = 500 120591min = 10 and 120591max =

100 The optimal 10 of individuals in the last generationin GA would be selected as the solution set of geneticoptimization which would be transformed into pheromone

values If manipulator 119894 was adjacent to manipulator 119895 ingenetic optimization solution the pheromone added 10 onthe path (119894 119895) and all the initial values of pheromonesshould be set like this When meeting one of the followingconditions theACA should stop (1)Thenumber of iterationsreaches antmax = 50 (2) For continuous three generations inthe iteration the improvement rate of offspring optimizationis less than 05

Click the button of algorithm running on the systeminterface the system calculates via the program after 50times genetic iterations and 11 times ant colony optimizationiteration the value of the optimal layout scheme is 2822The sequence of the manipulators is obtained that is 4 13 2 6 5 7 8 9 10 12 11 14 13 15 and 16 Accordingto the optimized sequence the layout scheme is shown inFigure 7

Computational Intelligence and Neuroscience 9

Table 4 The correlation between the 16 manipulators

119874119894119895

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 161 1 05 07 09 0 0 0 0 0 0 0 0 0 0 0 02 05 1 09 05 0 0 0 0 0 0 0 0 0 0 0 03 07 09 1 07 0 0 0 0 0 0 0 0 0 0 0 04 09 05 07 1 0 0 0 0 0 0 0 0 0 0 0 05 0 0 0 0 1 09 07 09 0 0 0 0 0 0 0 06 0 0 0 0 09 1 05 07 0 0 0 0 0 0 0 07 0 0 0 0 07 05 1 09 0 0 0 0 0 0 0 08 0 0 0 0 09 07 09 1 0 0 0 0 0 0 0 09 0 0 0 0 0 0 0 0 1 09 07 05 0 0 0 010 0 0 0 0 0 0 0 0 09 1 07 05 0 0 0 011 0 0 0 0 0 0 0 0 07 07 1 05 0 0 0 012 0 0 0 0 0 0 0 0 05 05 05 1 0 0 0 013 0 0 0 0 0 0 0 0 0 0 0 0 1 09 03 0314 0 0 0 0 0 0 0 0 0 0 0 0 09 1 03 0315 0 0 0 0 0 0 0 0 0 0 0 0 03 03 1 0916 0 0 0 0 0 0 0 0 0 0 0 0 03 03 09 1

Table 5 The comparison between GA ACA and ACA

Algorithm Layout scheme The value of layout scheme Number of iterationsGA 4 2 3 1 7 8 6 5 12 11 9 10 13 14 15 16 2814 82ACA 4 1 2 3 5 6 7 8 9 10 11 12 15 16 14 13 2773 75GA-ACA 4 1 2 3 7 8 5 6 12 9 10 11 15 16 13 14 2835 50 + 13

Figure 6 System interface of layout design

Because the proposed method is still in the research anddevelopment there is certain difference between the solutionand the actual situation Besides the human cognitive activ-ities and layout experience cannot be completely describedby the layout model layout scheme obtained by algorithmoptimization is close to the optimal solution rather than theoptimal solution In this stage the designer needs to adjustthe layout scheme according to the actual needs Similarlysort the manipulators on the console at the left hand Forthe sake of evaluating layout scheme intuitively computersimulation design modeling is proceeded Through detaileddesign the final layout scheme is shown in Figure 8

The CATIA software will be used to evaluate the resultof the layout CATIA V5 integrates four ergonomic moduleswhich can evaluate visibility accessibility the comfort ofposture (analyze the angle of each joint) and so on The bluearea in Figure 9 shows the reach envelope of drillerrsquos righthand Under natural state the drillerrsquos arm can operate themain manipulators on console This shows that the locationsof manipulators were in the human bodyrsquos correspondingrange of joint motion and in accord with the armrsquos motiontrail In Figure 10 the virtual human model simulates oneoperating posture of driller And the postural score analysisin Figure 11 shows the comfort score of lumbar vertebrathoracic vertebra head arm and forearmThe sores indicatethat this operating posture is comfortable the position of thismanipulator matches with the movement characteristics ofthe human body

33 Algorithm Comparison Using single GA ACA and GA-ACA hybrid algorithm to solve the above layout optimizationproblem respectively set the number of test times as 10each Result comparison is shown in Table 5 the columnof layout scheme shows the optimal result in 10 timesrsquorunning and the third and the fourth columns show theaverage value of layout scheme and the average number ofiterations As can be seen from the test result no matterthe solution speed or precision the GA-ACA is superiorto single GA and ACA in solving layout optimizationproblem

10 Computational Intelligence and Neuroscience

GEN E-STOPVFDE-STOP

LEFT RIGHT

E-BRAKEPARKINGBRAKE

RT INERTIABRAKE

RT SPEEDSETTING LIMIT

RT MOTORCONTROL

RT TORQUE ROTARYCATHEAD

CATHEAD CATHEAD

AIR SPARE1 SPARE2

ALARM

TOOLSSELECT

WORKINGBRAKE

Armrest0

200

0 200 400 600 800 10002004006008001000

400

600

800

Comfortableoperating range

operating rangeMaximum

1 3 24

56

7 8

9 10

1112

1314 15 16

SPINNER

Range 1

Range 2 Range 3

Range 4

y

x

Figure 7 Layout scheme of right console (after optimization)

Figure 8 Modeling and simulation of layout scheme

Figure 9 Analyze the right handrsquos reach envelope

34 Discussion Through reference to previous researchabout cognitive model this paper integrated their advantagesand put forward the cognitive model of human-machineinteraction interface suitable for cabin This model describedthe cognitive process of operators working in the cabinAnd then by analyzing the information processing processthe layout principles of human-machine interaction interfacewere summed up which would be quantified in the objectivefunction However human cognitive behavior process is verycomplicated part of the cognitive activities is difficult toexpress by formalized methods layout principles needed tobe improved further There were lots of influence factors in

Figure 10 The simulation of operating posture

Figure 11 The comfort evaluation of operating posture

actual engineering problem the layout problem was difficultto be described completely by mathematical model

It has become common that the bionic intelligencealgorithms were applied in the layout optimization designbut cognitive characteristics were seldom considered as theconstraints of layout optimization algorithm FurthermoreGA-ACA combined the advantages of two algorithms whichwould be more precise to solve layout optimization problemThe layout design of human-machine interaction interfaceof driller control room on drilling rig has illustrated theproposed method According to the characteristics of thehuman-machine interaction interface of different product

Computational Intelligence and Neuroscience 11

this method could be modified and adapted As a layoutergonomic design method it could assist designers andengineers to conduct human-machine interaction interfacedesign and improve the efficiency in layout design

4 Conclusion

Based on the theory of cognitive psychology according toWickens information processing model the cognitive modelof human-machine interaction interface was establishedConsidering peoplersquos information organization law visualsearch law user memory characteristics and so on thehuman-machine interaction interface should conform tooperatorsrsquo cognitive ability for interface information Basedon the cognitive model the layout principles of human-machine interaction interface were summarized as the layoutconstraintsThe problem of solving layout scheme was trans-formed into combinatorial optimization problem and GA-ACA was put forward to solve the problem which realizedthe algorithmization of artificial optimization process

Taking the layout of manipulators as example the objec-tive function of layout optimization was built according toeach principle Use GA to generate the solution of layoutscheme which would be transformed into initial pheromoneas ACA required Taking the difference value of comprehen-sive weight of the manipulators for the layout principles asheuristic information ofACA combinedwith the pheromoneprovided by GA the positive feedback optimization mecha-nism of ACA was used to solve further GA-ACA integratedthe complementary advantages of GA and ACA which hadgood optimizing performance and time performance andimproved the design efficiency Taking the 16 manipulators ofZJ1209000DB rig console as layout example layout designsystem of human-computer interaction interface for cabinwas developed by Visual Basic which validated the abovelayout optimization method

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgments

This research was supported by Open Fund (OGE201403-23) of Key Laboratory of Oil amp Gas Equipment Ministryof Education (Southwest Petroleum University) and by theOpen Research Subject (GY-14YB-31) of Key Laboratory(Research Base) of Industrial Design Industry Research Cen-ter Humanities and Social Science EducationDepartment ofSichuan Province

References

[1] J Z Cha X J Tang and Y P Lu ldquoSurvey on packing problemsrdquoJournal of Computer-AidedDesignampComputer Graphics vol 14pp 705ndash711 2002

[2] K A Dowsland and W B Dowsland ldquoPacking problemsrdquoEuropean Journal of Operational Research vol 56 no 1 pp 2ndash14 1992

[3] S Margaritis and NMarmaras ldquoSupporting the design of officelayout meeting ergonomics requirementsrdquo Applied Ergonomicsvol 38 no 6 pp 781ndash790 2007

[4] N Shariatzadeh G Sivard and D Chen ldquoSoftware evalu-ation criteria for rapid factory layout planning design andsimulationrdquo in Proceedings of the 45th CIRP Conference onManufacturing Systems pp 299ndash304 Athens GreeceMay 2012

[5] J Z Huo G Q Li H F Teng and Z G Sun ldquoHuman-computercooperative ant colonygenetic algorithm for satellite modulelayout designrdquo Chinese Journal of Mechanical Engineering vol41 no 3 pp 112ndash116 2005

[6] R Li D M Zhuang R Wang and L R Wang ldquoOptimizationabout the layout work of the steering arrangement in thecockpitrdquo Journal of System Simulation vol 16 pp 1305ndash13072004

[7] S Y Yan Y Chen and L Y Liang ldquoOptimization of controlslayout based on simulated annealing algorithmrdquo Nuclear PowerEngineering vol 35 no 1 pp 67ndash70 2014

[8] L C Zong C Ye S H Yu and D K Chen ldquoResearchand application of intelligent layout method in DSV cabinequipmentrdquo Shipbuilding of China vol 54 no 3 pp 147ndash1542013

[9] L C Zong S H Yu J B Sun L W Han and S S An ldquoStudyon cabin layout optimization with fish algorithmrdquo MechanicalScience and Technology for Aerospace Engineering vol 33 pp257ndash262 2014

[10] Y L Wang C Wang Z S Ji and X G Zhao ldquoA study onintelligent layout design of ship cabinrdquo Shipbuilding of Chinavol 54 pp 139ndash146 2013

[11] D HWolpert andWGMacready ldquoNo free lunch theorems foroptimizationrdquo IEEE Transactions on Evolutionary Computationvol 1 no 1 pp 67ndash82 1997

[12] F Q Zhao G Q Li C Yang A Abraham and H B LiuldquoA human-computer cooperative particle swarm optimizationbased immune algorithm for layout designrdquo Neurocomputingvol 132 pp 68ndash78 2014

[13] A Lodi S Martello and M Monaci ldquoTwo-dimensionalpacking problems a surveyrdquo European Journal of OperationalResearch vol 141 no 2 pp 241ndash252 2002

[14] J R AndersonCognitive Psychology and Itrsquos Implications editedby Y L Qin Posts amp Telecom Press 2012

[15] A Ghanbari S M R Kazemi F Mehmanpazir and M MNakhostin ldquoA cooperative ant colony optimization-geneticalgorithm approach for construction of energy demand fore-casting knowledge-based expert systemsrdquo Knowledge-BasedSystems vol 39 pp 194ndash206 2013

[16] J B Best Cognitive Psychology X T Huang Ed China LightIndustry Press Beijing China 2000

[17] C Wickens J G Hollands S Banbury and R ParasuramanEngineering Psychology amp Human Performance Pearson Lon-don UK 4th edition 2012

[18] L Y Sun Z X Li and T S Jin ldquoCognitive synthetic model andits application in HCIrdquo Journal of Xirsquoan Jiaotong University vol31 pp 74ndash80 1997

[19] Y P Zhang ldquoHuman-computer interface design based onknowledge of cognitive psychologyrdquo Computer Engineering andApplications vol 30 pp 105ndash107 2005

12 Computational Intelligence and Neuroscience

[20] Z Lan ldquoThe cognitive basis of software interface designrdquo ShanxiScience and Technology no 3 pp 41ndash42 1998

[21] X P Wang and L M Cao Genetic AlgorithmmdashTheory Appli-cation and Software Implementation Xirsquoan Jiaotong UniversityPress Xirsquoan China 2002

[22] E Bonabeau M Dorigo and G Theraulaz ldquoInspiration foroptimization from social insect behaviourrdquoNature vol 406 no6791 pp 39ndash42 2000

[23] M Dorigo and L M Gambardella ldquoAnt colonies for thetravelling salesman problemrdquo BioSystems vol 43 no 2 pp 73ndash81 1997

[24] MDorigo VManiezzo andA Colorni ldquoAnt system optimiza-tion by a colony of cooperating agentsrdquo IEEE Transactions onSystems Man and Cybernetics Part B Cybernetics vol 26 no1 pp 29ndash41 1996

[25] J-L Ding Z-Q Chen and Z-Z Yuan ldquoOn the Markovconvergence analysis for the combination of genetic algorithmand ant algorithmrdquo Acta Automatica Sinica vol 30 no 4 pp629ndash634 2004

[26] Z-J Lee S-F Su C-C Chuang and K-H Liu ldquoGeneticalgorithmwith ant colony optimization (GA-ACO) formultiplesequence alignmentrdquo Applied Soft Computing vol 8 no 1 pp55ndash78 2008

[27] Z Li C P Xu W Mo and G J Chen ldquoInitialization forsynchronous sequential circuits based on ant algorithm ampgenetic algorithmrdquo Acta Electronica Sinica vol 31 pp 1276ndash1280 2003

[28] Z-H Xiong S-K Li and J-H Chen ldquoHardwaresoftware par-titioning based on dynamic combination of genetic algorithmand ant algorithmrdquo Journal of Software vol 16 no 4 pp 503ndash512 2005

[29] J-L Ding Z-Q Chen and Z-Z Yuan ldquoOn the combinationof genetic algorithm and ant algorithmrdquo Journal of ComputerResearch and Development vol 40 no 9 pp 1351ndash1356 2003

[30] T Stutzle and H H Hoos ldquoMAX-MIN ant systemrdquo FutureGeneration Computer Systems vol 16 no 8 pp 889ndash914 2000

[31] T Stuetzle and H Hoos ldquoMAX-MIN Ant System and localsearch for the traveling salesman problemrdquo in Proceedings ofthe IEEE International Conference on Evolutionary Computation(ICEC rsquo97) pp 309ndash314 Indianapolis Indiana April 1997

Submit your manuscripts athttpwwwhindawicom

Computer Games Technology

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Distributed Sensor Networks

International Journal of

Advances in

FuzzySystems

Hindawi Publishing Corporationhttpwwwhindawicom

Volume 2014

International Journal of

ReconfigurableComputing

Hindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Applied Computational Intelligence and Soft Computing

thinspAdvancesthinspinthinsp

Artificial Intelligence

HindawithinspPublishingthinspCorporationhttpwwwhindawicom Volumethinsp2014

Advances inSoftware EngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Electrical and Computer Engineering

Journal of

Journal of

Computer Networks and Communications

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporation

httpwwwhindawicom Volume 2014

Advances in

Multimedia

International Journal of

Biomedical Imaging

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

ArtificialNeural Systems

Advances in

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

RoboticsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Computational Intelligence and Neuroscience

Industrial EngineeringJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Human-ComputerInteraction

Advances in

Computer EngineeringAdvances in

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Computational Intelligence and Neuroscience 7

Table 1 Partition coding for manipulators

The name of the manipulator Code The name of the manipulator Code

Range 1

E-BRAKE 1

Range 3

LEFT CATHEAD 9GEN E-STOP 2 RIGHT CATHEAD 10VFD E-STOP 3 ROTARY CATHEAD 11

PARKING BRAKE 4 ALARM 12

Range 2

RT MOTOR CONTROL 5

Range 4

AIR SPINNER 13RT INERTIA BRAKE 6 TOOLS SELECT 14RT SPEED SETTING 7 SPARE1 15RT TORQUE LIMIT 8 SPARE2 16

Ant chose next manipulator mainly according topheromone 120591

119894119895(119905) and heuristic information 120578

119894119895 120578119894119895

wasgiven by the layout problem to be solved and affected by V

119894119895

which remained unchanged during the operation processin algorithm Ants released pheromone on the path theypassed by In order to avoid too much residual informationto submerge heuristic information residual informationshould be upgraded After ants traversed through all themanipulators the pheromone updated in the environmentThe update equation is as follows

120591119894119895(119905 + 1) = (1 minus 120588) sdot 120591

119894119895(119905) + Δ120591

119894119895(119905)

Δ120591119894119895(119905) = sumΔ120591

119896

119894119895(119905)

Δ120591119896

119894119895(119905) =

119876

119871119896

(ant 119896 passes though (119894 119895))

0 (otherwise)

(10)

where 120588 (0 le 120588 le 1) represents the volatilization coefficientof pheromone Δ120591119896

119894119895(119905) represents the amount of pheromone

released between manipulator 119894 and manipulator 119895 by ant 119896in this cycle Δ120591

119894119895(119905) represents the increment of pheromone

between manipulator 119894 and manipulator 119895 after this cycle 119876is a constant which represents the amount of pheromone 119871

119896

represents the objective function value corresponding withthe layout of all the manipulators formed by ant 119896 traversedthrough in this cycle

Only the ant with optimal layout scheme could updatepheromone in one cycle The amount of pheromone on eachpath would be limited in scope of [120591min 120591max] If beyondthis range the pheromone would be mandatory set as 120591minor 120591max Compared with the standard ACA the improvedalgorithm prevented premature stagnation With all theants completing the traverse of all the manipulators thepheromone accumulated and volatilized continuously Untilreaching the number of iterations or a certain fitness valuethe optimal path formed by ants was the best layout scheme

3 Application Verification

31 Layout Design of Driller Control Room on Drilling RigTake the layout design of human-machine interaction inter-face of driller control room on drilling rig as an exampleto illustrate the proposed method The 16 manipulators of

Table 2 The size of each type of manipulators (mm)

The types of manipulators Size The codes of manipulatorsSwitch knobs Φ30 4 5 6 9 10 11 12 13 14 15 16Rotary knobs Φ50 7 8Buttons Φ40 1 2 3

ZJ1209000DB rig console would be arranged (the grey areaas shown in Figure 5) First of all the 16 manipulators needto be encoded As shown in Table 1 when encoding layoutPrinciple 1 needs to be reflected that is manipulators withsimilar function will be arranged in one area to reduce thesearch time for operators

It is necessary to simplify the layout area and layoutobjects when describing themathematical model to facilitatea digital description of design variables According to thehuman upper limb dimension to determine the layout spaceand simplify the layout area as rectangular area (450 times

400mm) there are three types of manipulators includingbuttons rotary knobs and switch knobs Their sizes areslightly different and the specific sizes are shown in Table 2The spaces between manipulators are set as equal and theinterval is 100mm After being simplified the layout ofmanipulators can be regarded as a scheduling problem firstusing GA-ACA to find a good sorting and then according tothe order to establish the actual layout

Drilling process is mainly divided into three workingconditions pull out of hole run in hole and normal drillingConsidering the principle of operating sequence in multipleworking conditions determine the manipulatorsrsquo operatingsequence and calculate each manipulatorrsquos weight relativeto Principle 2 Through questionnaires to consult drillersdetermine the relative judgment matrix of the manipulatorsrsquoimportance and frequency and calculate each manipulatorrsquosweight relative to Principle 3 Analytic hierarchy process willbe used to calculate each manipulatorrsquos weight of 119879

119894and

119878119894 and the calculation result is shown in Table 3 Finally

determine the relative correlation between the manipulatorsThe correlation between manipulator and itself is expressedwith 1 and the correlation between themanipulator and othermanipulators is expressed in decimal within 0sim1 The greaterthe correlation between manipulators the greater the corre-lation value The correlation between the 16 manipulators isshown in Table 4

8 Computational Intelligence and Neuroscience

Table 3 The manipulatorsrsquo weight relative to Principles 2 and 3

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16119879119894

0018 0018 0018 0055 0113 0076 0205 0163 0068 0068 0068 0016 0016 0098 0 0119878119894

0163 0115 0115 0115 0069 0069 0069 0069 0043 0043 0043 0013 0026 0026 0011 0011

LEFT DRUMSHIFT LOCK

AIR SLIPS

LEFT DRUMCLUTCH

AIR HORN

AD MODE AD MOTOR

LEFT AD LEFT ADCLUTCH

TOP SCREENWIPER CONTROL SPEED SETTING

PROTECTION RESET

SILIENCERESET

DW SPEEDCONTROL

KEYBOARD12 13 3 2 9

101 4

5 7

6 8

11 14

15 16

PARKINGBRAKE

RT INERTIABRAKE

RT SPEEDSETTING

TOOLSSELECT

E-BRAKE

VFDE-STOP E-STOPGEN

RT MOTORCONTROL

RT TORQUELIMIT

LEFT

RIGHTCATHEAD

CATHEAD

ROTARYCATHEAD

ALARM

AIRSPINNER

SPARE2SPARE1

Armrest

WORKINGBRAKERIGHT DRUM

SHIFT LOCKRIGHT DRUMCLUTCH

RIGHT ADSHIFT LOCH

SHIFT LOCH

RIGHT ADCLUTCH

FRONT SCREENWIPER CONTROL

Number 2 PUMP

Number 1 PUMP Number 3 PUMP

SPEED SETTING

SPEED SETTING

Figure 5 Layout schematic diagram of ZJ1209000DB rig console (before optimization)

32 Optimization Result After completing the above datapreparation as shown in Figure 6 the operating parametersof GA and ACA need to be set up before the computeraided optimization calculation proceeds First set the controlparameters of GA population size 119873 = 50 crossover rate119901119888= 06 mutation rate 119901

119898= 02 and number of partition

119904 = 4 Second set the control parameters ofACAThe amountof ants is119898 = 10 120572 = 1 120573 = 1 and 120588 = 05

In the system of GA-ACA calculation module set theend condition of GA minimum genetic iterations Genmin =

15 maximum genetic iterations Genmax = 50 minimumevolution rate Gene

119901= 3 and Gene

119902= 3 Set the control

parameters of pheromone 119876 = 500 120591min = 10 and 120591max =

100 The optimal 10 of individuals in the last generationin GA would be selected as the solution set of geneticoptimization which would be transformed into pheromone

values If manipulator 119894 was adjacent to manipulator 119895 ingenetic optimization solution the pheromone added 10 onthe path (119894 119895) and all the initial values of pheromonesshould be set like this When meeting one of the followingconditions theACA should stop (1)Thenumber of iterationsreaches antmax = 50 (2) For continuous three generations inthe iteration the improvement rate of offspring optimizationis less than 05

Click the button of algorithm running on the systeminterface the system calculates via the program after 50times genetic iterations and 11 times ant colony optimizationiteration the value of the optimal layout scheme is 2822The sequence of the manipulators is obtained that is 4 13 2 6 5 7 8 9 10 12 11 14 13 15 and 16 Accordingto the optimized sequence the layout scheme is shown inFigure 7

Computational Intelligence and Neuroscience 9

Table 4 The correlation between the 16 manipulators

119874119894119895

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 161 1 05 07 09 0 0 0 0 0 0 0 0 0 0 0 02 05 1 09 05 0 0 0 0 0 0 0 0 0 0 0 03 07 09 1 07 0 0 0 0 0 0 0 0 0 0 0 04 09 05 07 1 0 0 0 0 0 0 0 0 0 0 0 05 0 0 0 0 1 09 07 09 0 0 0 0 0 0 0 06 0 0 0 0 09 1 05 07 0 0 0 0 0 0 0 07 0 0 0 0 07 05 1 09 0 0 0 0 0 0 0 08 0 0 0 0 09 07 09 1 0 0 0 0 0 0 0 09 0 0 0 0 0 0 0 0 1 09 07 05 0 0 0 010 0 0 0 0 0 0 0 0 09 1 07 05 0 0 0 011 0 0 0 0 0 0 0 0 07 07 1 05 0 0 0 012 0 0 0 0 0 0 0 0 05 05 05 1 0 0 0 013 0 0 0 0 0 0 0 0 0 0 0 0 1 09 03 0314 0 0 0 0 0 0 0 0 0 0 0 0 09 1 03 0315 0 0 0 0 0 0 0 0 0 0 0 0 03 03 1 0916 0 0 0 0 0 0 0 0 0 0 0 0 03 03 09 1

Table 5 The comparison between GA ACA and ACA

Algorithm Layout scheme The value of layout scheme Number of iterationsGA 4 2 3 1 7 8 6 5 12 11 9 10 13 14 15 16 2814 82ACA 4 1 2 3 5 6 7 8 9 10 11 12 15 16 14 13 2773 75GA-ACA 4 1 2 3 7 8 5 6 12 9 10 11 15 16 13 14 2835 50 + 13

Figure 6 System interface of layout design

Because the proposed method is still in the research anddevelopment there is certain difference between the solutionand the actual situation Besides the human cognitive activ-ities and layout experience cannot be completely describedby the layout model layout scheme obtained by algorithmoptimization is close to the optimal solution rather than theoptimal solution In this stage the designer needs to adjustthe layout scheme according to the actual needs Similarlysort the manipulators on the console at the left hand Forthe sake of evaluating layout scheme intuitively computersimulation design modeling is proceeded Through detaileddesign the final layout scheme is shown in Figure 8

The CATIA software will be used to evaluate the resultof the layout CATIA V5 integrates four ergonomic moduleswhich can evaluate visibility accessibility the comfort ofposture (analyze the angle of each joint) and so on The bluearea in Figure 9 shows the reach envelope of drillerrsquos righthand Under natural state the drillerrsquos arm can operate themain manipulators on console This shows that the locationsof manipulators were in the human bodyrsquos correspondingrange of joint motion and in accord with the armrsquos motiontrail In Figure 10 the virtual human model simulates oneoperating posture of driller And the postural score analysisin Figure 11 shows the comfort score of lumbar vertebrathoracic vertebra head arm and forearmThe sores indicatethat this operating posture is comfortable the position of thismanipulator matches with the movement characteristics ofthe human body

33 Algorithm Comparison Using single GA ACA and GA-ACA hybrid algorithm to solve the above layout optimizationproblem respectively set the number of test times as 10each Result comparison is shown in Table 5 the columnof layout scheme shows the optimal result in 10 timesrsquorunning and the third and the fourth columns show theaverage value of layout scheme and the average number ofiterations As can be seen from the test result no matterthe solution speed or precision the GA-ACA is superiorto single GA and ACA in solving layout optimizationproblem

10 Computational Intelligence and Neuroscience

GEN E-STOPVFDE-STOP

LEFT RIGHT

E-BRAKEPARKINGBRAKE

RT INERTIABRAKE

RT SPEEDSETTING LIMIT

RT MOTORCONTROL

RT TORQUE ROTARYCATHEAD

CATHEAD CATHEAD

AIR SPARE1 SPARE2

ALARM

TOOLSSELECT

WORKINGBRAKE

Armrest0

200

0 200 400 600 800 10002004006008001000

400

600

800

Comfortableoperating range

operating rangeMaximum

1 3 24

56

7 8

9 10

1112

1314 15 16

SPINNER

Range 1

Range 2 Range 3

Range 4

y

x

Figure 7 Layout scheme of right console (after optimization)

Figure 8 Modeling and simulation of layout scheme

Figure 9 Analyze the right handrsquos reach envelope

34 Discussion Through reference to previous researchabout cognitive model this paper integrated their advantagesand put forward the cognitive model of human-machineinteraction interface suitable for cabin This model describedthe cognitive process of operators working in the cabinAnd then by analyzing the information processing processthe layout principles of human-machine interaction interfacewere summed up which would be quantified in the objectivefunction However human cognitive behavior process is verycomplicated part of the cognitive activities is difficult toexpress by formalized methods layout principles needed tobe improved further There were lots of influence factors in

Figure 10 The simulation of operating posture

Figure 11 The comfort evaluation of operating posture

actual engineering problem the layout problem was difficultto be described completely by mathematical model

It has become common that the bionic intelligencealgorithms were applied in the layout optimization designbut cognitive characteristics were seldom considered as theconstraints of layout optimization algorithm FurthermoreGA-ACA combined the advantages of two algorithms whichwould be more precise to solve layout optimization problemThe layout design of human-machine interaction interfaceof driller control room on drilling rig has illustrated theproposed method According to the characteristics of thehuman-machine interaction interface of different product

Computational Intelligence and Neuroscience 11

this method could be modified and adapted As a layoutergonomic design method it could assist designers andengineers to conduct human-machine interaction interfacedesign and improve the efficiency in layout design

4 Conclusion

Based on the theory of cognitive psychology according toWickens information processing model the cognitive modelof human-machine interaction interface was establishedConsidering peoplersquos information organization law visualsearch law user memory characteristics and so on thehuman-machine interaction interface should conform tooperatorsrsquo cognitive ability for interface information Basedon the cognitive model the layout principles of human-machine interaction interface were summarized as the layoutconstraintsThe problem of solving layout scheme was trans-formed into combinatorial optimization problem and GA-ACA was put forward to solve the problem which realizedthe algorithmization of artificial optimization process

Taking the layout of manipulators as example the objec-tive function of layout optimization was built according toeach principle Use GA to generate the solution of layoutscheme which would be transformed into initial pheromoneas ACA required Taking the difference value of comprehen-sive weight of the manipulators for the layout principles asheuristic information ofACA combinedwith the pheromoneprovided by GA the positive feedback optimization mecha-nism of ACA was used to solve further GA-ACA integratedthe complementary advantages of GA and ACA which hadgood optimizing performance and time performance andimproved the design efficiency Taking the 16 manipulators ofZJ1209000DB rig console as layout example layout designsystem of human-computer interaction interface for cabinwas developed by Visual Basic which validated the abovelayout optimization method

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgments

This research was supported by Open Fund (OGE201403-23) of Key Laboratory of Oil amp Gas Equipment Ministryof Education (Southwest Petroleum University) and by theOpen Research Subject (GY-14YB-31) of Key Laboratory(Research Base) of Industrial Design Industry Research Cen-ter Humanities and Social Science EducationDepartment ofSichuan Province

References

[1] J Z Cha X J Tang and Y P Lu ldquoSurvey on packing problemsrdquoJournal of Computer-AidedDesignampComputer Graphics vol 14pp 705ndash711 2002

[2] K A Dowsland and W B Dowsland ldquoPacking problemsrdquoEuropean Journal of Operational Research vol 56 no 1 pp 2ndash14 1992

[3] S Margaritis and NMarmaras ldquoSupporting the design of officelayout meeting ergonomics requirementsrdquo Applied Ergonomicsvol 38 no 6 pp 781ndash790 2007

[4] N Shariatzadeh G Sivard and D Chen ldquoSoftware evalu-ation criteria for rapid factory layout planning design andsimulationrdquo in Proceedings of the 45th CIRP Conference onManufacturing Systems pp 299ndash304 Athens GreeceMay 2012

[5] J Z Huo G Q Li H F Teng and Z G Sun ldquoHuman-computercooperative ant colonygenetic algorithm for satellite modulelayout designrdquo Chinese Journal of Mechanical Engineering vol41 no 3 pp 112ndash116 2005

[6] R Li D M Zhuang R Wang and L R Wang ldquoOptimizationabout the layout work of the steering arrangement in thecockpitrdquo Journal of System Simulation vol 16 pp 1305ndash13072004

[7] S Y Yan Y Chen and L Y Liang ldquoOptimization of controlslayout based on simulated annealing algorithmrdquo Nuclear PowerEngineering vol 35 no 1 pp 67ndash70 2014

[8] L C Zong C Ye S H Yu and D K Chen ldquoResearchand application of intelligent layout method in DSV cabinequipmentrdquo Shipbuilding of China vol 54 no 3 pp 147ndash1542013

[9] L C Zong S H Yu J B Sun L W Han and S S An ldquoStudyon cabin layout optimization with fish algorithmrdquo MechanicalScience and Technology for Aerospace Engineering vol 33 pp257ndash262 2014

[10] Y L Wang C Wang Z S Ji and X G Zhao ldquoA study onintelligent layout design of ship cabinrdquo Shipbuilding of Chinavol 54 pp 139ndash146 2013

[11] D HWolpert andWGMacready ldquoNo free lunch theorems foroptimizationrdquo IEEE Transactions on Evolutionary Computationvol 1 no 1 pp 67ndash82 1997

[12] F Q Zhao G Q Li C Yang A Abraham and H B LiuldquoA human-computer cooperative particle swarm optimizationbased immune algorithm for layout designrdquo Neurocomputingvol 132 pp 68ndash78 2014

[13] A Lodi S Martello and M Monaci ldquoTwo-dimensionalpacking problems a surveyrdquo European Journal of OperationalResearch vol 141 no 2 pp 241ndash252 2002

[14] J R AndersonCognitive Psychology and Itrsquos Implications editedby Y L Qin Posts amp Telecom Press 2012

[15] A Ghanbari S M R Kazemi F Mehmanpazir and M MNakhostin ldquoA cooperative ant colony optimization-geneticalgorithm approach for construction of energy demand fore-casting knowledge-based expert systemsrdquo Knowledge-BasedSystems vol 39 pp 194ndash206 2013

[16] J B Best Cognitive Psychology X T Huang Ed China LightIndustry Press Beijing China 2000

[17] C Wickens J G Hollands S Banbury and R ParasuramanEngineering Psychology amp Human Performance Pearson Lon-don UK 4th edition 2012

[18] L Y Sun Z X Li and T S Jin ldquoCognitive synthetic model andits application in HCIrdquo Journal of Xirsquoan Jiaotong University vol31 pp 74ndash80 1997

[19] Y P Zhang ldquoHuman-computer interface design based onknowledge of cognitive psychologyrdquo Computer Engineering andApplications vol 30 pp 105ndash107 2005

12 Computational Intelligence and Neuroscience

[20] Z Lan ldquoThe cognitive basis of software interface designrdquo ShanxiScience and Technology no 3 pp 41ndash42 1998

[21] X P Wang and L M Cao Genetic AlgorithmmdashTheory Appli-cation and Software Implementation Xirsquoan Jiaotong UniversityPress Xirsquoan China 2002

[22] E Bonabeau M Dorigo and G Theraulaz ldquoInspiration foroptimization from social insect behaviourrdquoNature vol 406 no6791 pp 39ndash42 2000

[23] M Dorigo and L M Gambardella ldquoAnt colonies for thetravelling salesman problemrdquo BioSystems vol 43 no 2 pp 73ndash81 1997

[24] MDorigo VManiezzo andA Colorni ldquoAnt system optimiza-tion by a colony of cooperating agentsrdquo IEEE Transactions onSystems Man and Cybernetics Part B Cybernetics vol 26 no1 pp 29ndash41 1996

[25] J-L Ding Z-Q Chen and Z-Z Yuan ldquoOn the Markovconvergence analysis for the combination of genetic algorithmand ant algorithmrdquo Acta Automatica Sinica vol 30 no 4 pp629ndash634 2004

[26] Z-J Lee S-F Su C-C Chuang and K-H Liu ldquoGeneticalgorithmwith ant colony optimization (GA-ACO) formultiplesequence alignmentrdquo Applied Soft Computing vol 8 no 1 pp55ndash78 2008

[27] Z Li C P Xu W Mo and G J Chen ldquoInitialization forsynchronous sequential circuits based on ant algorithm ampgenetic algorithmrdquo Acta Electronica Sinica vol 31 pp 1276ndash1280 2003

[28] Z-H Xiong S-K Li and J-H Chen ldquoHardwaresoftware par-titioning based on dynamic combination of genetic algorithmand ant algorithmrdquo Journal of Software vol 16 no 4 pp 503ndash512 2005

[29] J-L Ding Z-Q Chen and Z-Z Yuan ldquoOn the combinationof genetic algorithm and ant algorithmrdquo Journal of ComputerResearch and Development vol 40 no 9 pp 1351ndash1356 2003

[30] T Stutzle and H H Hoos ldquoMAX-MIN ant systemrdquo FutureGeneration Computer Systems vol 16 no 8 pp 889ndash914 2000

[31] T Stuetzle and H Hoos ldquoMAX-MIN Ant System and localsearch for the traveling salesman problemrdquo in Proceedings ofthe IEEE International Conference on Evolutionary Computation(ICEC rsquo97) pp 309ndash314 Indianapolis Indiana April 1997

Submit your manuscripts athttpwwwhindawicom

Computer Games Technology

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Distributed Sensor Networks

International Journal of

Advances in

FuzzySystems

Hindawi Publishing Corporationhttpwwwhindawicom

Volume 2014

International Journal of

ReconfigurableComputing

Hindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Applied Computational Intelligence and Soft Computing

thinspAdvancesthinspinthinsp

Artificial Intelligence

HindawithinspPublishingthinspCorporationhttpwwwhindawicom Volumethinsp2014

Advances inSoftware EngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Electrical and Computer Engineering

Journal of

Journal of

Computer Networks and Communications

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporation

httpwwwhindawicom Volume 2014

Advances in

Multimedia

International Journal of

Biomedical Imaging

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

ArtificialNeural Systems

Advances in

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

RoboticsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Computational Intelligence and Neuroscience

Industrial EngineeringJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Human-ComputerInteraction

Advances in

Computer EngineeringAdvances in

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

8 Computational Intelligence and Neuroscience

Table 3 The manipulatorsrsquo weight relative to Principles 2 and 3

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16119879119894

0018 0018 0018 0055 0113 0076 0205 0163 0068 0068 0068 0016 0016 0098 0 0119878119894

0163 0115 0115 0115 0069 0069 0069 0069 0043 0043 0043 0013 0026 0026 0011 0011

LEFT DRUMSHIFT LOCK

AIR SLIPS

LEFT DRUMCLUTCH

AIR HORN

AD MODE AD MOTOR

LEFT AD LEFT ADCLUTCH

TOP SCREENWIPER CONTROL SPEED SETTING

PROTECTION RESET

SILIENCERESET

DW SPEEDCONTROL

KEYBOARD12 13 3 2 9

101 4

5 7

6 8

11 14

15 16

PARKINGBRAKE

RT INERTIABRAKE

RT SPEEDSETTING

TOOLSSELECT

E-BRAKE

VFDE-STOP E-STOPGEN

RT MOTORCONTROL

RT TORQUELIMIT

LEFT

RIGHTCATHEAD

CATHEAD

ROTARYCATHEAD

ALARM

AIRSPINNER

SPARE2SPARE1

Armrest

WORKINGBRAKERIGHT DRUM

SHIFT LOCKRIGHT DRUMCLUTCH

RIGHT ADSHIFT LOCH

SHIFT LOCH

RIGHT ADCLUTCH

FRONT SCREENWIPER CONTROL

Number 2 PUMP

Number 1 PUMP Number 3 PUMP

SPEED SETTING

SPEED SETTING

Figure 5 Layout schematic diagram of ZJ1209000DB rig console (before optimization)

32 Optimization Result After completing the above datapreparation as shown in Figure 6 the operating parametersof GA and ACA need to be set up before the computeraided optimization calculation proceeds First set the controlparameters of GA population size 119873 = 50 crossover rate119901119888= 06 mutation rate 119901

119898= 02 and number of partition

119904 = 4 Second set the control parameters ofACAThe amountof ants is119898 = 10 120572 = 1 120573 = 1 and 120588 = 05

In the system of GA-ACA calculation module set theend condition of GA minimum genetic iterations Genmin =

15 maximum genetic iterations Genmax = 50 minimumevolution rate Gene

119901= 3 and Gene

119902= 3 Set the control

parameters of pheromone 119876 = 500 120591min = 10 and 120591max =

100 The optimal 10 of individuals in the last generationin GA would be selected as the solution set of geneticoptimization which would be transformed into pheromone

values If manipulator 119894 was adjacent to manipulator 119895 ingenetic optimization solution the pheromone added 10 onthe path (119894 119895) and all the initial values of pheromonesshould be set like this When meeting one of the followingconditions theACA should stop (1)Thenumber of iterationsreaches antmax = 50 (2) For continuous three generations inthe iteration the improvement rate of offspring optimizationis less than 05

Click the button of algorithm running on the systeminterface the system calculates via the program after 50times genetic iterations and 11 times ant colony optimizationiteration the value of the optimal layout scheme is 2822The sequence of the manipulators is obtained that is 4 13 2 6 5 7 8 9 10 12 11 14 13 15 and 16 Accordingto the optimized sequence the layout scheme is shown inFigure 7

Computational Intelligence and Neuroscience 9

Table 4 The correlation between the 16 manipulators

119874119894119895

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 161 1 05 07 09 0 0 0 0 0 0 0 0 0 0 0 02 05 1 09 05 0 0 0 0 0 0 0 0 0 0 0 03 07 09 1 07 0 0 0 0 0 0 0 0 0 0 0 04 09 05 07 1 0 0 0 0 0 0 0 0 0 0 0 05 0 0 0 0 1 09 07 09 0 0 0 0 0 0 0 06 0 0 0 0 09 1 05 07 0 0 0 0 0 0 0 07 0 0 0 0 07 05 1 09 0 0 0 0 0 0 0 08 0 0 0 0 09 07 09 1 0 0 0 0 0 0 0 09 0 0 0 0 0 0 0 0 1 09 07 05 0 0 0 010 0 0 0 0 0 0 0 0 09 1 07 05 0 0 0 011 0 0 0 0 0 0 0 0 07 07 1 05 0 0 0 012 0 0 0 0 0 0 0 0 05 05 05 1 0 0 0 013 0 0 0 0 0 0 0 0 0 0 0 0 1 09 03 0314 0 0 0 0 0 0 0 0 0 0 0 0 09 1 03 0315 0 0 0 0 0 0 0 0 0 0 0 0 03 03 1 0916 0 0 0 0 0 0 0 0 0 0 0 0 03 03 09 1

Table 5 The comparison between GA ACA and ACA

Algorithm Layout scheme The value of layout scheme Number of iterationsGA 4 2 3 1 7 8 6 5 12 11 9 10 13 14 15 16 2814 82ACA 4 1 2 3 5 6 7 8 9 10 11 12 15 16 14 13 2773 75GA-ACA 4 1 2 3 7 8 5 6 12 9 10 11 15 16 13 14 2835 50 + 13

Figure 6 System interface of layout design

Because the proposed method is still in the research anddevelopment there is certain difference between the solutionand the actual situation Besides the human cognitive activ-ities and layout experience cannot be completely describedby the layout model layout scheme obtained by algorithmoptimization is close to the optimal solution rather than theoptimal solution In this stage the designer needs to adjustthe layout scheme according to the actual needs Similarlysort the manipulators on the console at the left hand Forthe sake of evaluating layout scheme intuitively computersimulation design modeling is proceeded Through detaileddesign the final layout scheme is shown in Figure 8

The CATIA software will be used to evaluate the resultof the layout CATIA V5 integrates four ergonomic moduleswhich can evaluate visibility accessibility the comfort ofposture (analyze the angle of each joint) and so on The bluearea in Figure 9 shows the reach envelope of drillerrsquos righthand Under natural state the drillerrsquos arm can operate themain manipulators on console This shows that the locationsof manipulators were in the human bodyrsquos correspondingrange of joint motion and in accord with the armrsquos motiontrail In Figure 10 the virtual human model simulates oneoperating posture of driller And the postural score analysisin Figure 11 shows the comfort score of lumbar vertebrathoracic vertebra head arm and forearmThe sores indicatethat this operating posture is comfortable the position of thismanipulator matches with the movement characteristics ofthe human body

33 Algorithm Comparison Using single GA ACA and GA-ACA hybrid algorithm to solve the above layout optimizationproblem respectively set the number of test times as 10each Result comparison is shown in Table 5 the columnof layout scheme shows the optimal result in 10 timesrsquorunning and the third and the fourth columns show theaverage value of layout scheme and the average number ofiterations As can be seen from the test result no matterthe solution speed or precision the GA-ACA is superiorto single GA and ACA in solving layout optimizationproblem

10 Computational Intelligence and Neuroscience

GEN E-STOPVFDE-STOP

LEFT RIGHT

E-BRAKEPARKINGBRAKE

RT INERTIABRAKE

RT SPEEDSETTING LIMIT

RT MOTORCONTROL

RT TORQUE ROTARYCATHEAD

CATHEAD CATHEAD

AIR SPARE1 SPARE2

ALARM

TOOLSSELECT

WORKINGBRAKE

Armrest0

200

0 200 400 600 800 10002004006008001000

400

600

800

Comfortableoperating range

operating rangeMaximum

1 3 24

56

7 8

9 10

1112

1314 15 16

SPINNER

Range 1

Range 2 Range 3

Range 4

y

x

Figure 7 Layout scheme of right console (after optimization)

Figure 8 Modeling and simulation of layout scheme

Figure 9 Analyze the right handrsquos reach envelope

34 Discussion Through reference to previous researchabout cognitive model this paper integrated their advantagesand put forward the cognitive model of human-machineinteraction interface suitable for cabin This model describedthe cognitive process of operators working in the cabinAnd then by analyzing the information processing processthe layout principles of human-machine interaction interfacewere summed up which would be quantified in the objectivefunction However human cognitive behavior process is verycomplicated part of the cognitive activities is difficult toexpress by formalized methods layout principles needed tobe improved further There were lots of influence factors in

Figure 10 The simulation of operating posture

Figure 11 The comfort evaluation of operating posture

actual engineering problem the layout problem was difficultto be described completely by mathematical model

It has become common that the bionic intelligencealgorithms were applied in the layout optimization designbut cognitive characteristics were seldom considered as theconstraints of layout optimization algorithm FurthermoreGA-ACA combined the advantages of two algorithms whichwould be more precise to solve layout optimization problemThe layout design of human-machine interaction interfaceof driller control room on drilling rig has illustrated theproposed method According to the characteristics of thehuman-machine interaction interface of different product

Computational Intelligence and Neuroscience 11

this method could be modified and adapted As a layoutergonomic design method it could assist designers andengineers to conduct human-machine interaction interfacedesign and improve the efficiency in layout design

4 Conclusion

Based on the theory of cognitive psychology according toWickens information processing model the cognitive modelof human-machine interaction interface was establishedConsidering peoplersquos information organization law visualsearch law user memory characteristics and so on thehuman-machine interaction interface should conform tooperatorsrsquo cognitive ability for interface information Basedon the cognitive model the layout principles of human-machine interaction interface were summarized as the layoutconstraintsThe problem of solving layout scheme was trans-formed into combinatorial optimization problem and GA-ACA was put forward to solve the problem which realizedthe algorithmization of artificial optimization process

Taking the layout of manipulators as example the objec-tive function of layout optimization was built according toeach principle Use GA to generate the solution of layoutscheme which would be transformed into initial pheromoneas ACA required Taking the difference value of comprehen-sive weight of the manipulators for the layout principles asheuristic information ofACA combinedwith the pheromoneprovided by GA the positive feedback optimization mecha-nism of ACA was used to solve further GA-ACA integratedthe complementary advantages of GA and ACA which hadgood optimizing performance and time performance andimproved the design efficiency Taking the 16 manipulators ofZJ1209000DB rig console as layout example layout designsystem of human-computer interaction interface for cabinwas developed by Visual Basic which validated the abovelayout optimization method

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgments

This research was supported by Open Fund (OGE201403-23) of Key Laboratory of Oil amp Gas Equipment Ministryof Education (Southwest Petroleum University) and by theOpen Research Subject (GY-14YB-31) of Key Laboratory(Research Base) of Industrial Design Industry Research Cen-ter Humanities and Social Science EducationDepartment ofSichuan Province

References

[1] J Z Cha X J Tang and Y P Lu ldquoSurvey on packing problemsrdquoJournal of Computer-AidedDesignampComputer Graphics vol 14pp 705ndash711 2002

[2] K A Dowsland and W B Dowsland ldquoPacking problemsrdquoEuropean Journal of Operational Research vol 56 no 1 pp 2ndash14 1992

[3] S Margaritis and NMarmaras ldquoSupporting the design of officelayout meeting ergonomics requirementsrdquo Applied Ergonomicsvol 38 no 6 pp 781ndash790 2007

[4] N Shariatzadeh G Sivard and D Chen ldquoSoftware evalu-ation criteria for rapid factory layout planning design andsimulationrdquo in Proceedings of the 45th CIRP Conference onManufacturing Systems pp 299ndash304 Athens GreeceMay 2012

[5] J Z Huo G Q Li H F Teng and Z G Sun ldquoHuman-computercooperative ant colonygenetic algorithm for satellite modulelayout designrdquo Chinese Journal of Mechanical Engineering vol41 no 3 pp 112ndash116 2005

[6] R Li D M Zhuang R Wang and L R Wang ldquoOptimizationabout the layout work of the steering arrangement in thecockpitrdquo Journal of System Simulation vol 16 pp 1305ndash13072004

[7] S Y Yan Y Chen and L Y Liang ldquoOptimization of controlslayout based on simulated annealing algorithmrdquo Nuclear PowerEngineering vol 35 no 1 pp 67ndash70 2014

[8] L C Zong C Ye S H Yu and D K Chen ldquoResearchand application of intelligent layout method in DSV cabinequipmentrdquo Shipbuilding of China vol 54 no 3 pp 147ndash1542013

[9] L C Zong S H Yu J B Sun L W Han and S S An ldquoStudyon cabin layout optimization with fish algorithmrdquo MechanicalScience and Technology for Aerospace Engineering vol 33 pp257ndash262 2014

[10] Y L Wang C Wang Z S Ji and X G Zhao ldquoA study onintelligent layout design of ship cabinrdquo Shipbuilding of Chinavol 54 pp 139ndash146 2013

[11] D HWolpert andWGMacready ldquoNo free lunch theorems foroptimizationrdquo IEEE Transactions on Evolutionary Computationvol 1 no 1 pp 67ndash82 1997

[12] F Q Zhao G Q Li C Yang A Abraham and H B LiuldquoA human-computer cooperative particle swarm optimizationbased immune algorithm for layout designrdquo Neurocomputingvol 132 pp 68ndash78 2014

[13] A Lodi S Martello and M Monaci ldquoTwo-dimensionalpacking problems a surveyrdquo European Journal of OperationalResearch vol 141 no 2 pp 241ndash252 2002

[14] J R AndersonCognitive Psychology and Itrsquos Implications editedby Y L Qin Posts amp Telecom Press 2012

[15] A Ghanbari S M R Kazemi F Mehmanpazir and M MNakhostin ldquoA cooperative ant colony optimization-geneticalgorithm approach for construction of energy demand fore-casting knowledge-based expert systemsrdquo Knowledge-BasedSystems vol 39 pp 194ndash206 2013

[16] J B Best Cognitive Psychology X T Huang Ed China LightIndustry Press Beijing China 2000

[17] C Wickens J G Hollands S Banbury and R ParasuramanEngineering Psychology amp Human Performance Pearson Lon-don UK 4th edition 2012

[18] L Y Sun Z X Li and T S Jin ldquoCognitive synthetic model andits application in HCIrdquo Journal of Xirsquoan Jiaotong University vol31 pp 74ndash80 1997

[19] Y P Zhang ldquoHuman-computer interface design based onknowledge of cognitive psychologyrdquo Computer Engineering andApplications vol 30 pp 105ndash107 2005

12 Computational Intelligence and Neuroscience

[20] Z Lan ldquoThe cognitive basis of software interface designrdquo ShanxiScience and Technology no 3 pp 41ndash42 1998

[21] X P Wang and L M Cao Genetic AlgorithmmdashTheory Appli-cation and Software Implementation Xirsquoan Jiaotong UniversityPress Xirsquoan China 2002

[22] E Bonabeau M Dorigo and G Theraulaz ldquoInspiration foroptimization from social insect behaviourrdquoNature vol 406 no6791 pp 39ndash42 2000

[23] M Dorigo and L M Gambardella ldquoAnt colonies for thetravelling salesman problemrdquo BioSystems vol 43 no 2 pp 73ndash81 1997

[24] MDorigo VManiezzo andA Colorni ldquoAnt system optimiza-tion by a colony of cooperating agentsrdquo IEEE Transactions onSystems Man and Cybernetics Part B Cybernetics vol 26 no1 pp 29ndash41 1996

[25] J-L Ding Z-Q Chen and Z-Z Yuan ldquoOn the Markovconvergence analysis for the combination of genetic algorithmand ant algorithmrdquo Acta Automatica Sinica vol 30 no 4 pp629ndash634 2004

[26] Z-J Lee S-F Su C-C Chuang and K-H Liu ldquoGeneticalgorithmwith ant colony optimization (GA-ACO) formultiplesequence alignmentrdquo Applied Soft Computing vol 8 no 1 pp55ndash78 2008

[27] Z Li C P Xu W Mo and G J Chen ldquoInitialization forsynchronous sequential circuits based on ant algorithm ampgenetic algorithmrdquo Acta Electronica Sinica vol 31 pp 1276ndash1280 2003

[28] Z-H Xiong S-K Li and J-H Chen ldquoHardwaresoftware par-titioning based on dynamic combination of genetic algorithmand ant algorithmrdquo Journal of Software vol 16 no 4 pp 503ndash512 2005

[29] J-L Ding Z-Q Chen and Z-Z Yuan ldquoOn the combinationof genetic algorithm and ant algorithmrdquo Journal of ComputerResearch and Development vol 40 no 9 pp 1351ndash1356 2003

[30] T Stutzle and H H Hoos ldquoMAX-MIN ant systemrdquo FutureGeneration Computer Systems vol 16 no 8 pp 889ndash914 2000

[31] T Stuetzle and H Hoos ldquoMAX-MIN Ant System and localsearch for the traveling salesman problemrdquo in Proceedings ofthe IEEE International Conference on Evolutionary Computation(ICEC rsquo97) pp 309ndash314 Indianapolis Indiana April 1997

Submit your manuscripts athttpwwwhindawicom

Computer Games Technology

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Distributed Sensor Networks

International Journal of

Advances in

FuzzySystems

Hindawi Publishing Corporationhttpwwwhindawicom

Volume 2014

International Journal of

ReconfigurableComputing

Hindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Applied Computational Intelligence and Soft Computing

thinspAdvancesthinspinthinsp

Artificial Intelligence

HindawithinspPublishingthinspCorporationhttpwwwhindawicom Volumethinsp2014

Advances inSoftware EngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Electrical and Computer Engineering

Journal of

Journal of

Computer Networks and Communications

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporation

httpwwwhindawicom Volume 2014

Advances in

Multimedia

International Journal of

Biomedical Imaging

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

ArtificialNeural Systems

Advances in

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

RoboticsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Computational Intelligence and Neuroscience

Industrial EngineeringJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Human-ComputerInteraction

Advances in

Computer EngineeringAdvances in

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Computational Intelligence and Neuroscience 9

Table 4 The correlation between the 16 manipulators

119874119894119895

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 161 1 05 07 09 0 0 0 0 0 0 0 0 0 0 0 02 05 1 09 05 0 0 0 0 0 0 0 0 0 0 0 03 07 09 1 07 0 0 0 0 0 0 0 0 0 0 0 04 09 05 07 1 0 0 0 0 0 0 0 0 0 0 0 05 0 0 0 0 1 09 07 09 0 0 0 0 0 0 0 06 0 0 0 0 09 1 05 07 0 0 0 0 0 0 0 07 0 0 0 0 07 05 1 09 0 0 0 0 0 0 0 08 0 0 0 0 09 07 09 1 0 0 0 0 0 0 0 09 0 0 0 0 0 0 0 0 1 09 07 05 0 0 0 010 0 0 0 0 0 0 0 0 09 1 07 05 0 0 0 011 0 0 0 0 0 0 0 0 07 07 1 05 0 0 0 012 0 0 0 0 0 0 0 0 05 05 05 1 0 0 0 013 0 0 0 0 0 0 0 0 0 0 0 0 1 09 03 0314 0 0 0 0 0 0 0 0 0 0 0 0 09 1 03 0315 0 0 0 0 0 0 0 0 0 0 0 0 03 03 1 0916 0 0 0 0 0 0 0 0 0 0 0 0 03 03 09 1

Table 5 The comparison between GA ACA and ACA

Algorithm Layout scheme The value of layout scheme Number of iterationsGA 4 2 3 1 7 8 6 5 12 11 9 10 13 14 15 16 2814 82ACA 4 1 2 3 5 6 7 8 9 10 11 12 15 16 14 13 2773 75GA-ACA 4 1 2 3 7 8 5 6 12 9 10 11 15 16 13 14 2835 50 + 13

Figure 6 System interface of layout design

Because the proposed method is still in the research anddevelopment there is certain difference between the solutionand the actual situation Besides the human cognitive activ-ities and layout experience cannot be completely describedby the layout model layout scheme obtained by algorithmoptimization is close to the optimal solution rather than theoptimal solution In this stage the designer needs to adjustthe layout scheme according to the actual needs Similarlysort the manipulators on the console at the left hand Forthe sake of evaluating layout scheme intuitively computersimulation design modeling is proceeded Through detaileddesign the final layout scheme is shown in Figure 8

The CATIA software will be used to evaluate the resultof the layout CATIA V5 integrates four ergonomic moduleswhich can evaluate visibility accessibility the comfort ofposture (analyze the angle of each joint) and so on The bluearea in Figure 9 shows the reach envelope of drillerrsquos righthand Under natural state the drillerrsquos arm can operate themain manipulators on console This shows that the locationsof manipulators were in the human bodyrsquos correspondingrange of joint motion and in accord with the armrsquos motiontrail In Figure 10 the virtual human model simulates oneoperating posture of driller And the postural score analysisin Figure 11 shows the comfort score of lumbar vertebrathoracic vertebra head arm and forearmThe sores indicatethat this operating posture is comfortable the position of thismanipulator matches with the movement characteristics ofthe human body

33 Algorithm Comparison Using single GA ACA and GA-ACA hybrid algorithm to solve the above layout optimizationproblem respectively set the number of test times as 10each Result comparison is shown in Table 5 the columnof layout scheme shows the optimal result in 10 timesrsquorunning and the third and the fourth columns show theaverage value of layout scheme and the average number ofiterations As can be seen from the test result no matterthe solution speed or precision the GA-ACA is superiorto single GA and ACA in solving layout optimizationproblem

10 Computational Intelligence and Neuroscience

GEN E-STOPVFDE-STOP

LEFT RIGHT

E-BRAKEPARKINGBRAKE

RT INERTIABRAKE

RT SPEEDSETTING LIMIT

RT MOTORCONTROL

RT TORQUE ROTARYCATHEAD

CATHEAD CATHEAD

AIR SPARE1 SPARE2

ALARM

TOOLSSELECT

WORKINGBRAKE

Armrest0

200

0 200 400 600 800 10002004006008001000

400

600

800

Comfortableoperating range

operating rangeMaximum

1 3 24

56

7 8

9 10

1112

1314 15 16

SPINNER

Range 1

Range 2 Range 3

Range 4

y

x

Figure 7 Layout scheme of right console (after optimization)

Figure 8 Modeling and simulation of layout scheme

Figure 9 Analyze the right handrsquos reach envelope

34 Discussion Through reference to previous researchabout cognitive model this paper integrated their advantagesand put forward the cognitive model of human-machineinteraction interface suitable for cabin This model describedthe cognitive process of operators working in the cabinAnd then by analyzing the information processing processthe layout principles of human-machine interaction interfacewere summed up which would be quantified in the objectivefunction However human cognitive behavior process is verycomplicated part of the cognitive activities is difficult toexpress by formalized methods layout principles needed tobe improved further There were lots of influence factors in

Figure 10 The simulation of operating posture

Figure 11 The comfort evaluation of operating posture

actual engineering problem the layout problem was difficultto be described completely by mathematical model

It has become common that the bionic intelligencealgorithms were applied in the layout optimization designbut cognitive characteristics were seldom considered as theconstraints of layout optimization algorithm FurthermoreGA-ACA combined the advantages of two algorithms whichwould be more precise to solve layout optimization problemThe layout design of human-machine interaction interfaceof driller control room on drilling rig has illustrated theproposed method According to the characteristics of thehuman-machine interaction interface of different product

Computational Intelligence and Neuroscience 11

this method could be modified and adapted As a layoutergonomic design method it could assist designers andengineers to conduct human-machine interaction interfacedesign and improve the efficiency in layout design

4 Conclusion

Based on the theory of cognitive psychology according toWickens information processing model the cognitive modelof human-machine interaction interface was establishedConsidering peoplersquos information organization law visualsearch law user memory characteristics and so on thehuman-machine interaction interface should conform tooperatorsrsquo cognitive ability for interface information Basedon the cognitive model the layout principles of human-machine interaction interface were summarized as the layoutconstraintsThe problem of solving layout scheme was trans-formed into combinatorial optimization problem and GA-ACA was put forward to solve the problem which realizedthe algorithmization of artificial optimization process

Taking the layout of manipulators as example the objec-tive function of layout optimization was built according toeach principle Use GA to generate the solution of layoutscheme which would be transformed into initial pheromoneas ACA required Taking the difference value of comprehen-sive weight of the manipulators for the layout principles asheuristic information ofACA combinedwith the pheromoneprovided by GA the positive feedback optimization mecha-nism of ACA was used to solve further GA-ACA integratedthe complementary advantages of GA and ACA which hadgood optimizing performance and time performance andimproved the design efficiency Taking the 16 manipulators ofZJ1209000DB rig console as layout example layout designsystem of human-computer interaction interface for cabinwas developed by Visual Basic which validated the abovelayout optimization method

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgments

This research was supported by Open Fund (OGE201403-23) of Key Laboratory of Oil amp Gas Equipment Ministryof Education (Southwest Petroleum University) and by theOpen Research Subject (GY-14YB-31) of Key Laboratory(Research Base) of Industrial Design Industry Research Cen-ter Humanities and Social Science EducationDepartment ofSichuan Province

References

[1] J Z Cha X J Tang and Y P Lu ldquoSurvey on packing problemsrdquoJournal of Computer-AidedDesignampComputer Graphics vol 14pp 705ndash711 2002

[2] K A Dowsland and W B Dowsland ldquoPacking problemsrdquoEuropean Journal of Operational Research vol 56 no 1 pp 2ndash14 1992

[3] S Margaritis and NMarmaras ldquoSupporting the design of officelayout meeting ergonomics requirementsrdquo Applied Ergonomicsvol 38 no 6 pp 781ndash790 2007

[4] N Shariatzadeh G Sivard and D Chen ldquoSoftware evalu-ation criteria for rapid factory layout planning design andsimulationrdquo in Proceedings of the 45th CIRP Conference onManufacturing Systems pp 299ndash304 Athens GreeceMay 2012

[5] J Z Huo G Q Li H F Teng and Z G Sun ldquoHuman-computercooperative ant colonygenetic algorithm for satellite modulelayout designrdquo Chinese Journal of Mechanical Engineering vol41 no 3 pp 112ndash116 2005

[6] R Li D M Zhuang R Wang and L R Wang ldquoOptimizationabout the layout work of the steering arrangement in thecockpitrdquo Journal of System Simulation vol 16 pp 1305ndash13072004

[7] S Y Yan Y Chen and L Y Liang ldquoOptimization of controlslayout based on simulated annealing algorithmrdquo Nuclear PowerEngineering vol 35 no 1 pp 67ndash70 2014

[8] L C Zong C Ye S H Yu and D K Chen ldquoResearchand application of intelligent layout method in DSV cabinequipmentrdquo Shipbuilding of China vol 54 no 3 pp 147ndash1542013

[9] L C Zong S H Yu J B Sun L W Han and S S An ldquoStudyon cabin layout optimization with fish algorithmrdquo MechanicalScience and Technology for Aerospace Engineering vol 33 pp257ndash262 2014

[10] Y L Wang C Wang Z S Ji and X G Zhao ldquoA study onintelligent layout design of ship cabinrdquo Shipbuilding of Chinavol 54 pp 139ndash146 2013

[11] D HWolpert andWGMacready ldquoNo free lunch theorems foroptimizationrdquo IEEE Transactions on Evolutionary Computationvol 1 no 1 pp 67ndash82 1997

[12] F Q Zhao G Q Li C Yang A Abraham and H B LiuldquoA human-computer cooperative particle swarm optimizationbased immune algorithm for layout designrdquo Neurocomputingvol 132 pp 68ndash78 2014

[13] A Lodi S Martello and M Monaci ldquoTwo-dimensionalpacking problems a surveyrdquo European Journal of OperationalResearch vol 141 no 2 pp 241ndash252 2002

[14] J R AndersonCognitive Psychology and Itrsquos Implications editedby Y L Qin Posts amp Telecom Press 2012

[15] A Ghanbari S M R Kazemi F Mehmanpazir and M MNakhostin ldquoA cooperative ant colony optimization-geneticalgorithm approach for construction of energy demand fore-casting knowledge-based expert systemsrdquo Knowledge-BasedSystems vol 39 pp 194ndash206 2013

[16] J B Best Cognitive Psychology X T Huang Ed China LightIndustry Press Beijing China 2000

[17] C Wickens J G Hollands S Banbury and R ParasuramanEngineering Psychology amp Human Performance Pearson Lon-don UK 4th edition 2012

[18] L Y Sun Z X Li and T S Jin ldquoCognitive synthetic model andits application in HCIrdquo Journal of Xirsquoan Jiaotong University vol31 pp 74ndash80 1997

[19] Y P Zhang ldquoHuman-computer interface design based onknowledge of cognitive psychologyrdquo Computer Engineering andApplications vol 30 pp 105ndash107 2005

12 Computational Intelligence and Neuroscience

[20] Z Lan ldquoThe cognitive basis of software interface designrdquo ShanxiScience and Technology no 3 pp 41ndash42 1998

[21] X P Wang and L M Cao Genetic AlgorithmmdashTheory Appli-cation and Software Implementation Xirsquoan Jiaotong UniversityPress Xirsquoan China 2002

[22] E Bonabeau M Dorigo and G Theraulaz ldquoInspiration foroptimization from social insect behaviourrdquoNature vol 406 no6791 pp 39ndash42 2000

[23] M Dorigo and L M Gambardella ldquoAnt colonies for thetravelling salesman problemrdquo BioSystems vol 43 no 2 pp 73ndash81 1997

[24] MDorigo VManiezzo andA Colorni ldquoAnt system optimiza-tion by a colony of cooperating agentsrdquo IEEE Transactions onSystems Man and Cybernetics Part B Cybernetics vol 26 no1 pp 29ndash41 1996

[25] J-L Ding Z-Q Chen and Z-Z Yuan ldquoOn the Markovconvergence analysis for the combination of genetic algorithmand ant algorithmrdquo Acta Automatica Sinica vol 30 no 4 pp629ndash634 2004

[26] Z-J Lee S-F Su C-C Chuang and K-H Liu ldquoGeneticalgorithmwith ant colony optimization (GA-ACO) formultiplesequence alignmentrdquo Applied Soft Computing vol 8 no 1 pp55ndash78 2008

[27] Z Li C P Xu W Mo and G J Chen ldquoInitialization forsynchronous sequential circuits based on ant algorithm ampgenetic algorithmrdquo Acta Electronica Sinica vol 31 pp 1276ndash1280 2003

[28] Z-H Xiong S-K Li and J-H Chen ldquoHardwaresoftware par-titioning based on dynamic combination of genetic algorithmand ant algorithmrdquo Journal of Software vol 16 no 4 pp 503ndash512 2005

[29] J-L Ding Z-Q Chen and Z-Z Yuan ldquoOn the combinationof genetic algorithm and ant algorithmrdquo Journal of ComputerResearch and Development vol 40 no 9 pp 1351ndash1356 2003

[30] T Stutzle and H H Hoos ldquoMAX-MIN ant systemrdquo FutureGeneration Computer Systems vol 16 no 8 pp 889ndash914 2000

[31] T Stuetzle and H Hoos ldquoMAX-MIN Ant System and localsearch for the traveling salesman problemrdquo in Proceedings ofthe IEEE International Conference on Evolutionary Computation(ICEC rsquo97) pp 309ndash314 Indianapolis Indiana April 1997

Submit your manuscripts athttpwwwhindawicom

Computer Games Technology

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Distributed Sensor Networks

International Journal of

Advances in

FuzzySystems

Hindawi Publishing Corporationhttpwwwhindawicom

Volume 2014

International Journal of

ReconfigurableComputing

Hindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Applied Computational Intelligence and Soft Computing

thinspAdvancesthinspinthinsp

Artificial Intelligence

HindawithinspPublishingthinspCorporationhttpwwwhindawicom Volumethinsp2014

Advances inSoftware EngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Electrical and Computer Engineering

Journal of

Journal of

Computer Networks and Communications

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporation

httpwwwhindawicom Volume 2014

Advances in

Multimedia

International Journal of

Biomedical Imaging

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

ArtificialNeural Systems

Advances in

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

RoboticsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Computational Intelligence and Neuroscience

Industrial EngineeringJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Human-ComputerInteraction

Advances in

Computer EngineeringAdvances in

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

10 Computational Intelligence and Neuroscience

GEN E-STOPVFDE-STOP

LEFT RIGHT

E-BRAKEPARKINGBRAKE

RT INERTIABRAKE

RT SPEEDSETTING LIMIT

RT MOTORCONTROL

RT TORQUE ROTARYCATHEAD

CATHEAD CATHEAD

AIR SPARE1 SPARE2

ALARM

TOOLSSELECT

WORKINGBRAKE

Armrest0

200

0 200 400 600 800 10002004006008001000

400

600

800

Comfortableoperating range

operating rangeMaximum

1 3 24

56

7 8

9 10

1112

1314 15 16

SPINNER

Range 1

Range 2 Range 3

Range 4

y

x

Figure 7 Layout scheme of right console (after optimization)

Figure 8 Modeling and simulation of layout scheme

Figure 9 Analyze the right handrsquos reach envelope

34 Discussion Through reference to previous researchabout cognitive model this paper integrated their advantagesand put forward the cognitive model of human-machineinteraction interface suitable for cabin This model describedthe cognitive process of operators working in the cabinAnd then by analyzing the information processing processthe layout principles of human-machine interaction interfacewere summed up which would be quantified in the objectivefunction However human cognitive behavior process is verycomplicated part of the cognitive activities is difficult toexpress by formalized methods layout principles needed tobe improved further There were lots of influence factors in

Figure 10 The simulation of operating posture

Figure 11 The comfort evaluation of operating posture

actual engineering problem the layout problem was difficultto be described completely by mathematical model

It has become common that the bionic intelligencealgorithms were applied in the layout optimization designbut cognitive characteristics were seldom considered as theconstraints of layout optimization algorithm FurthermoreGA-ACA combined the advantages of two algorithms whichwould be more precise to solve layout optimization problemThe layout design of human-machine interaction interfaceof driller control room on drilling rig has illustrated theproposed method According to the characteristics of thehuman-machine interaction interface of different product

Computational Intelligence and Neuroscience 11

this method could be modified and adapted As a layoutergonomic design method it could assist designers andengineers to conduct human-machine interaction interfacedesign and improve the efficiency in layout design

4 Conclusion

Based on the theory of cognitive psychology according toWickens information processing model the cognitive modelof human-machine interaction interface was establishedConsidering peoplersquos information organization law visualsearch law user memory characteristics and so on thehuman-machine interaction interface should conform tooperatorsrsquo cognitive ability for interface information Basedon the cognitive model the layout principles of human-machine interaction interface were summarized as the layoutconstraintsThe problem of solving layout scheme was trans-formed into combinatorial optimization problem and GA-ACA was put forward to solve the problem which realizedthe algorithmization of artificial optimization process

Taking the layout of manipulators as example the objec-tive function of layout optimization was built according toeach principle Use GA to generate the solution of layoutscheme which would be transformed into initial pheromoneas ACA required Taking the difference value of comprehen-sive weight of the manipulators for the layout principles asheuristic information ofACA combinedwith the pheromoneprovided by GA the positive feedback optimization mecha-nism of ACA was used to solve further GA-ACA integratedthe complementary advantages of GA and ACA which hadgood optimizing performance and time performance andimproved the design efficiency Taking the 16 manipulators ofZJ1209000DB rig console as layout example layout designsystem of human-computer interaction interface for cabinwas developed by Visual Basic which validated the abovelayout optimization method

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgments

This research was supported by Open Fund (OGE201403-23) of Key Laboratory of Oil amp Gas Equipment Ministryof Education (Southwest Petroleum University) and by theOpen Research Subject (GY-14YB-31) of Key Laboratory(Research Base) of Industrial Design Industry Research Cen-ter Humanities and Social Science EducationDepartment ofSichuan Province

References

[1] J Z Cha X J Tang and Y P Lu ldquoSurvey on packing problemsrdquoJournal of Computer-AidedDesignampComputer Graphics vol 14pp 705ndash711 2002

[2] K A Dowsland and W B Dowsland ldquoPacking problemsrdquoEuropean Journal of Operational Research vol 56 no 1 pp 2ndash14 1992

[3] S Margaritis and NMarmaras ldquoSupporting the design of officelayout meeting ergonomics requirementsrdquo Applied Ergonomicsvol 38 no 6 pp 781ndash790 2007

[4] N Shariatzadeh G Sivard and D Chen ldquoSoftware evalu-ation criteria for rapid factory layout planning design andsimulationrdquo in Proceedings of the 45th CIRP Conference onManufacturing Systems pp 299ndash304 Athens GreeceMay 2012

[5] J Z Huo G Q Li H F Teng and Z G Sun ldquoHuman-computercooperative ant colonygenetic algorithm for satellite modulelayout designrdquo Chinese Journal of Mechanical Engineering vol41 no 3 pp 112ndash116 2005

[6] R Li D M Zhuang R Wang and L R Wang ldquoOptimizationabout the layout work of the steering arrangement in thecockpitrdquo Journal of System Simulation vol 16 pp 1305ndash13072004

[7] S Y Yan Y Chen and L Y Liang ldquoOptimization of controlslayout based on simulated annealing algorithmrdquo Nuclear PowerEngineering vol 35 no 1 pp 67ndash70 2014

[8] L C Zong C Ye S H Yu and D K Chen ldquoResearchand application of intelligent layout method in DSV cabinequipmentrdquo Shipbuilding of China vol 54 no 3 pp 147ndash1542013

[9] L C Zong S H Yu J B Sun L W Han and S S An ldquoStudyon cabin layout optimization with fish algorithmrdquo MechanicalScience and Technology for Aerospace Engineering vol 33 pp257ndash262 2014

[10] Y L Wang C Wang Z S Ji and X G Zhao ldquoA study onintelligent layout design of ship cabinrdquo Shipbuilding of Chinavol 54 pp 139ndash146 2013

[11] D HWolpert andWGMacready ldquoNo free lunch theorems foroptimizationrdquo IEEE Transactions on Evolutionary Computationvol 1 no 1 pp 67ndash82 1997

[12] F Q Zhao G Q Li C Yang A Abraham and H B LiuldquoA human-computer cooperative particle swarm optimizationbased immune algorithm for layout designrdquo Neurocomputingvol 132 pp 68ndash78 2014

[13] A Lodi S Martello and M Monaci ldquoTwo-dimensionalpacking problems a surveyrdquo European Journal of OperationalResearch vol 141 no 2 pp 241ndash252 2002

[14] J R AndersonCognitive Psychology and Itrsquos Implications editedby Y L Qin Posts amp Telecom Press 2012

[15] A Ghanbari S M R Kazemi F Mehmanpazir and M MNakhostin ldquoA cooperative ant colony optimization-geneticalgorithm approach for construction of energy demand fore-casting knowledge-based expert systemsrdquo Knowledge-BasedSystems vol 39 pp 194ndash206 2013

[16] J B Best Cognitive Psychology X T Huang Ed China LightIndustry Press Beijing China 2000

[17] C Wickens J G Hollands S Banbury and R ParasuramanEngineering Psychology amp Human Performance Pearson Lon-don UK 4th edition 2012

[18] L Y Sun Z X Li and T S Jin ldquoCognitive synthetic model andits application in HCIrdquo Journal of Xirsquoan Jiaotong University vol31 pp 74ndash80 1997

[19] Y P Zhang ldquoHuman-computer interface design based onknowledge of cognitive psychologyrdquo Computer Engineering andApplications vol 30 pp 105ndash107 2005

12 Computational Intelligence and Neuroscience

[20] Z Lan ldquoThe cognitive basis of software interface designrdquo ShanxiScience and Technology no 3 pp 41ndash42 1998

[21] X P Wang and L M Cao Genetic AlgorithmmdashTheory Appli-cation and Software Implementation Xirsquoan Jiaotong UniversityPress Xirsquoan China 2002

[22] E Bonabeau M Dorigo and G Theraulaz ldquoInspiration foroptimization from social insect behaviourrdquoNature vol 406 no6791 pp 39ndash42 2000

[23] M Dorigo and L M Gambardella ldquoAnt colonies for thetravelling salesman problemrdquo BioSystems vol 43 no 2 pp 73ndash81 1997

[24] MDorigo VManiezzo andA Colorni ldquoAnt system optimiza-tion by a colony of cooperating agentsrdquo IEEE Transactions onSystems Man and Cybernetics Part B Cybernetics vol 26 no1 pp 29ndash41 1996

[25] J-L Ding Z-Q Chen and Z-Z Yuan ldquoOn the Markovconvergence analysis for the combination of genetic algorithmand ant algorithmrdquo Acta Automatica Sinica vol 30 no 4 pp629ndash634 2004

[26] Z-J Lee S-F Su C-C Chuang and K-H Liu ldquoGeneticalgorithmwith ant colony optimization (GA-ACO) formultiplesequence alignmentrdquo Applied Soft Computing vol 8 no 1 pp55ndash78 2008

[27] Z Li C P Xu W Mo and G J Chen ldquoInitialization forsynchronous sequential circuits based on ant algorithm ampgenetic algorithmrdquo Acta Electronica Sinica vol 31 pp 1276ndash1280 2003

[28] Z-H Xiong S-K Li and J-H Chen ldquoHardwaresoftware par-titioning based on dynamic combination of genetic algorithmand ant algorithmrdquo Journal of Software vol 16 no 4 pp 503ndash512 2005

[29] J-L Ding Z-Q Chen and Z-Z Yuan ldquoOn the combinationof genetic algorithm and ant algorithmrdquo Journal of ComputerResearch and Development vol 40 no 9 pp 1351ndash1356 2003

[30] T Stutzle and H H Hoos ldquoMAX-MIN ant systemrdquo FutureGeneration Computer Systems vol 16 no 8 pp 889ndash914 2000

[31] T Stuetzle and H Hoos ldquoMAX-MIN Ant System and localsearch for the traveling salesman problemrdquo in Proceedings ofthe IEEE International Conference on Evolutionary Computation(ICEC rsquo97) pp 309ndash314 Indianapolis Indiana April 1997

Submit your manuscripts athttpwwwhindawicom

Computer Games Technology

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Distributed Sensor Networks

International Journal of

Advances in

FuzzySystems

Hindawi Publishing Corporationhttpwwwhindawicom

Volume 2014

International Journal of

ReconfigurableComputing

Hindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Applied Computational Intelligence and Soft Computing

thinspAdvancesthinspinthinsp

Artificial Intelligence

HindawithinspPublishingthinspCorporationhttpwwwhindawicom Volumethinsp2014

Advances inSoftware EngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Electrical and Computer Engineering

Journal of

Journal of

Computer Networks and Communications

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporation

httpwwwhindawicom Volume 2014

Advances in

Multimedia

International Journal of

Biomedical Imaging

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

ArtificialNeural Systems

Advances in

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

RoboticsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Computational Intelligence and Neuroscience

Industrial EngineeringJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Human-ComputerInteraction

Advances in

Computer EngineeringAdvances in

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Computational Intelligence and Neuroscience 11

this method could be modified and adapted As a layoutergonomic design method it could assist designers andengineers to conduct human-machine interaction interfacedesign and improve the efficiency in layout design

4 Conclusion

Based on the theory of cognitive psychology according toWickens information processing model the cognitive modelof human-machine interaction interface was establishedConsidering peoplersquos information organization law visualsearch law user memory characteristics and so on thehuman-machine interaction interface should conform tooperatorsrsquo cognitive ability for interface information Basedon the cognitive model the layout principles of human-machine interaction interface were summarized as the layoutconstraintsThe problem of solving layout scheme was trans-formed into combinatorial optimization problem and GA-ACA was put forward to solve the problem which realizedthe algorithmization of artificial optimization process

Taking the layout of manipulators as example the objec-tive function of layout optimization was built according toeach principle Use GA to generate the solution of layoutscheme which would be transformed into initial pheromoneas ACA required Taking the difference value of comprehen-sive weight of the manipulators for the layout principles asheuristic information ofACA combinedwith the pheromoneprovided by GA the positive feedback optimization mecha-nism of ACA was used to solve further GA-ACA integratedthe complementary advantages of GA and ACA which hadgood optimizing performance and time performance andimproved the design efficiency Taking the 16 manipulators ofZJ1209000DB rig console as layout example layout designsystem of human-computer interaction interface for cabinwas developed by Visual Basic which validated the abovelayout optimization method

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgments

This research was supported by Open Fund (OGE201403-23) of Key Laboratory of Oil amp Gas Equipment Ministryof Education (Southwest Petroleum University) and by theOpen Research Subject (GY-14YB-31) of Key Laboratory(Research Base) of Industrial Design Industry Research Cen-ter Humanities and Social Science EducationDepartment ofSichuan Province

References

[1] J Z Cha X J Tang and Y P Lu ldquoSurvey on packing problemsrdquoJournal of Computer-AidedDesignampComputer Graphics vol 14pp 705ndash711 2002

[2] K A Dowsland and W B Dowsland ldquoPacking problemsrdquoEuropean Journal of Operational Research vol 56 no 1 pp 2ndash14 1992

[3] S Margaritis and NMarmaras ldquoSupporting the design of officelayout meeting ergonomics requirementsrdquo Applied Ergonomicsvol 38 no 6 pp 781ndash790 2007

[4] N Shariatzadeh G Sivard and D Chen ldquoSoftware evalu-ation criteria for rapid factory layout planning design andsimulationrdquo in Proceedings of the 45th CIRP Conference onManufacturing Systems pp 299ndash304 Athens GreeceMay 2012

[5] J Z Huo G Q Li H F Teng and Z G Sun ldquoHuman-computercooperative ant colonygenetic algorithm for satellite modulelayout designrdquo Chinese Journal of Mechanical Engineering vol41 no 3 pp 112ndash116 2005

[6] R Li D M Zhuang R Wang and L R Wang ldquoOptimizationabout the layout work of the steering arrangement in thecockpitrdquo Journal of System Simulation vol 16 pp 1305ndash13072004

[7] S Y Yan Y Chen and L Y Liang ldquoOptimization of controlslayout based on simulated annealing algorithmrdquo Nuclear PowerEngineering vol 35 no 1 pp 67ndash70 2014

[8] L C Zong C Ye S H Yu and D K Chen ldquoResearchand application of intelligent layout method in DSV cabinequipmentrdquo Shipbuilding of China vol 54 no 3 pp 147ndash1542013

[9] L C Zong S H Yu J B Sun L W Han and S S An ldquoStudyon cabin layout optimization with fish algorithmrdquo MechanicalScience and Technology for Aerospace Engineering vol 33 pp257ndash262 2014

[10] Y L Wang C Wang Z S Ji and X G Zhao ldquoA study onintelligent layout design of ship cabinrdquo Shipbuilding of Chinavol 54 pp 139ndash146 2013

[11] D HWolpert andWGMacready ldquoNo free lunch theorems foroptimizationrdquo IEEE Transactions on Evolutionary Computationvol 1 no 1 pp 67ndash82 1997

[12] F Q Zhao G Q Li C Yang A Abraham and H B LiuldquoA human-computer cooperative particle swarm optimizationbased immune algorithm for layout designrdquo Neurocomputingvol 132 pp 68ndash78 2014

[13] A Lodi S Martello and M Monaci ldquoTwo-dimensionalpacking problems a surveyrdquo European Journal of OperationalResearch vol 141 no 2 pp 241ndash252 2002

[14] J R AndersonCognitive Psychology and Itrsquos Implications editedby Y L Qin Posts amp Telecom Press 2012

[15] A Ghanbari S M R Kazemi F Mehmanpazir and M MNakhostin ldquoA cooperative ant colony optimization-geneticalgorithm approach for construction of energy demand fore-casting knowledge-based expert systemsrdquo Knowledge-BasedSystems vol 39 pp 194ndash206 2013

[16] J B Best Cognitive Psychology X T Huang Ed China LightIndustry Press Beijing China 2000

[17] C Wickens J G Hollands S Banbury and R ParasuramanEngineering Psychology amp Human Performance Pearson Lon-don UK 4th edition 2012

[18] L Y Sun Z X Li and T S Jin ldquoCognitive synthetic model andits application in HCIrdquo Journal of Xirsquoan Jiaotong University vol31 pp 74ndash80 1997

[19] Y P Zhang ldquoHuman-computer interface design based onknowledge of cognitive psychologyrdquo Computer Engineering andApplications vol 30 pp 105ndash107 2005

12 Computational Intelligence and Neuroscience

[20] Z Lan ldquoThe cognitive basis of software interface designrdquo ShanxiScience and Technology no 3 pp 41ndash42 1998

[21] X P Wang and L M Cao Genetic AlgorithmmdashTheory Appli-cation and Software Implementation Xirsquoan Jiaotong UniversityPress Xirsquoan China 2002

[22] E Bonabeau M Dorigo and G Theraulaz ldquoInspiration foroptimization from social insect behaviourrdquoNature vol 406 no6791 pp 39ndash42 2000

[23] M Dorigo and L M Gambardella ldquoAnt colonies for thetravelling salesman problemrdquo BioSystems vol 43 no 2 pp 73ndash81 1997

[24] MDorigo VManiezzo andA Colorni ldquoAnt system optimiza-tion by a colony of cooperating agentsrdquo IEEE Transactions onSystems Man and Cybernetics Part B Cybernetics vol 26 no1 pp 29ndash41 1996

[25] J-L Ding Z-Q Chen and Z-Z Yuan ldquoOn the Markovconvergence analysis for the combination of genetic algorithmand ant algorithmrdquo Acta Automatica Sinica vol 30 no 4 pp629ndash634 2004

[26] Z-J Lee S-F Su C-C Chuang and K-H Liu ldquoGeneticalgorithmwith ant colony optimization (GA-ACO) formultiplesequence alignmentrdquo Applied Soft Computing vol 8 no 1 pp55ndash78 2008

[27] Z Li C P Xu W Mo and G J Chen ldquoInitialization forsynchronous sequential circuits based on ant algorithm ampgenetic algorithmrdquo Acta Electronica Sinica vol 31 pp 1276ndash1280 2003

[28] Z-H Xiong S-K Li and J-H Chen ldquoHardwaresoftware par-titioning based on dynamic combination of genetic algorithmand ant algorithmrdquo Journal of Software vol 16 no 4 pp 503ndash512 2005

[29] J-L Ding Z-Q Chen and Z-Z Yuan ldquoOn the combinationof genetic algorithm and ant algorithmrdquo Journal of ComputerResearch and Development vol 40 no 9 pp 1351ndash1356 2003

[30] T Stutzle and H H Hoos ldquoMAX-MIN ant systemrdquo FutureGeneration Computer Systems vol 16 no 8 pp 889ndash914 2000

[31] T Stuetzle and H Hoos ldquoMAX-MIN Ant System and localsearch for the traveling salesman problemrdquo in Proceedings ofthe IEEE International Conference on Evolutionary Computation(ICEC rsquo97) pp 309ndash314 Indianapolis Indiana April 1997

Submit your manuscripts athttpwwwhindawicom

Computer Games Technology

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Distributed Sensor Networks

International Journal of

Advances in

FuzzySystems

Hindawi Publishing Corporationhttpwwwhindawicom

Volume 2014

International Journal of

ReconfigurableComputing

Hindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Applied Computational Intelligence and Soft Computing

thinspAdvancesthinspinthinsp

Artificial Intelligence

HindawithinspPublishingthinspCorporationhttpwwwhindawicom Volumethinsp2014

Advances inSoftware EngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Electrical and Computer Engineering

Journal of

Journal of

Computer Networks and Communications

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporation

httpwwwhindawicom Volume 2014

Advances in

Multimedia

International Journal of

Biomedical Imaging

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

ArtificialNeural Systems

Advances in

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

RoboticsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Computational Intelligence and Neuroscience

Industrial EngineeringJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Human-ComputerInteraction

Advances in

Computer EngineeringAdvances in

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

12 Computational Intelligence and Neuroscience

[20] Z Lan ldquoThe cognitive basis of software interface designrdquo ShanxiScience and Technology no 3 pp 41ndash42 1998

[21] X P Wang and L M Cao Genetic AlgorithmmdashTheory Appli-cation and Software Implementation Xirsquoan Jiaotong UniversityPress Xirsquoan China 2002

[22] E Bonabeau M Dorigo and G Theraulaz ldquoInspiration foroptimization from social insect behaviourrdquoNature vol 406 no6791 pp 39ndash42 2000

[23] M Dorigo and L M Gambardella ldquoAnt colonies for thetravelling salesman problemrdquo BioSystems vol 43 no 2 pp 73ndash81 1997

[24] MDorigo VManiezzo andA Colorni ldquoAnt system optimiza-tion by a colony of cooperating agentsrdquo IEEE Transactions onSystems Man and Cybernetics Part B Cybernetics vol 26 no1 pp 29ndash41 1996

[25] J-L Ding Z-Q Chen and Z-Z Yuan ldquoOn the Markovconvergence analysis for the combination of genetic algorithmand ant algorithmrdquo Acta Automatica Sinica vol 30 no 4 pp629ndash634 2004

[26] Z-J Lee S-F Su C-C Chuang and K-H Liu ldquoGeneticalgorithmwith ant colony optimization (GA-ACO) formultiplesequence alignmentrdquo Applied Soft Computing vol 8 no 1 pp55ndash78 2008

[27] Z Li C P Xu W Mo and G J Chen ldquoInitialization forsynchronous sequential circuits based on ant algorithm ampgenetic algorithmrdquo Acta Electronica Sinica vol 31 pp 1276ndash1280 2003

[28] Z-H Xiong S-K Li and J-H Chen ldquoHardwaresoftware par-titioning based on dynamic combination of genetic algorithmand ant algorithmrdquo Journal of Software vol 16 no 4 pp 503ndash512 2005

[29] J-L Ding Z-Q Chen and Z-Z Yuan ldquoOn the combinationof genetic algorithm and ant algorithmrdquo Journal of ComputerResearch and Development vol 40 no 9 pp 1351ndash1356 2003

[30] T Stutzle and H H Hoos ldquoMAX-MIN ant systemrdquo FutureGeneration Computer Systems vol 16 no 8 pp 889ndash914 2000

[31] T Stuetzle and H Hoos ldquoMAX-MIN Ant System and localsearch for the traveling salesman problemrdquo in Proceedings ofthe IEEE International Conference on Evolutionary Computation(ICEC rsquo97) pp 309ndash314 Indianapolis Indiana April 1997

Submit your manuscripts athttpwwwhindawicom

Computer Games Technology

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Distributed Sensor Networks

International Journal of

Advances in

FuzzySystems

Hindawi Publishing Corporationhttpwwwhindawicom

Volume 2014

International Journal of

ReconfigurableComputing

Hindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Applied Computational Intelligence and Soft Computing

thinspAdvancesthinspinthinsp

Artificial Intelligence

HindawithinspPublishingthinspCorporationhttpwwwhindawicom Volumethinsp2014

Advances inSoftware EngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Electrical and Computer Engineering

Journal of

Journal of

Computer Networks and Communications

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporation

httpwwwhindawicom Volume 2014

Advances in

Multimedia

International Journal of

Biomedical Imaging

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

ArtificialNeural Systems

Advances in

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

RoboticsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Computational Intelligence and Neuroscience

Industrial EngineeringJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Human-ComputerInteraction

Advances in

Computer EngineeringAdvances in

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Submit your manuscripts athttpwwwhindawicom

Computer Games Technology

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Distributed Sensor Networks

International Journal of

Advances in

FuzzySystems

Hindawi Publishing Corporationhttpwwwhindawicom

Volume 2014

International Journal of

ReconfigurableComputing

Hindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Applied Computational Intelligence and Soft Computing

thinspAdvancesthinspinthinsp

Artificial Intelligence

HindawithinspPublishingthinspCorporationhttpwwwhindawicom Volumethinsp2014

Advances inSoftware EngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Electrical and Computer Engineering

Journal of

Journal of

Computer Networks and Communications

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporation

httpwwwhindawicom Volume 2014

Advances in

Multimedia

International Journal of

Biomedical Imaging

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

ArtificialNeural Systems

Advances in

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

RoboticsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Computational Intelligence and Neuroscience

Industrial EngineeringJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Human-ComputerInteraction

Advances in

Computer EngineeringAdvances in

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014