CHAPTER 4 ARTIFICIAL BEE COLONY WIRELESS...
Transcript of CHAPTER 4 ARTIFICIAL BEE COLONY WIRELESS...
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CHAPTER 4
ARTIFICIAL BEE COLONY WIRELESS CLUSTERING
4.1. INTRODUCTION TO ARTIFICIAL BEE COLONY
Swarm intelligence is a new discipline of study that contains a
relatively optimal approach for problem solving which are the imitations
inspired from the social behaviour of insects and animals, for example,
Ant Colony Optimization (ACO) algorithm, Honey Bee Algorithms, Fire
inspiration for the design of novel algorithms, which is the solution for
optimization and distributed control problems. The Honey Bee Mating
algorithm is the growing technique, which is proposed in late 2005, for
many engineering applications.
Honey bees are insects that live in large colonies (around
50,000 bees as a colony) usually containing one queen and her progeny,
some 20,000 40,000 female workers and 200 300 male drones. Michael
et al (2010) has a detailed study of honeybee in the biological aspect and
about the foraging behaviour. There are many syndromes observed like
aggression syndrome, waggling dance, from the honey bee colony which
is used for solving optimization problems. Although honey bees are
depicted in many cave paintings dated from 6000BC, Aristotle made the
first recorded observation of bee behaviour.
The honey bee is a diffuse creature which can extend itself over
long distances in multiple directions in order to find a large number of
food sources and at the same time to find the best food source from the
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collection of food sources. For example, the flower patches with plentiful
amounts of nectar or pollen that can be collected with less effort should
be visited by more bees, whereas patches with less nectar or pollen should
receive fewer bees.
The foraging process begins in a colony by scout bees, these
bees are sent to search for promising flower patches. Scout bees search
randomly from one patch to another. When they return to the hive, those
scout bees that identified from a patch which is rated above a certain
threshold which is measured as a combination of some constituents, such
as sugar content, deposit their nectar or pollen and go to the "dance floor"
to perform a dance known as the "waggle dance".
This dance is essential for colony communication, and contains
three vital pieces of information regarding flower patches: the direction in
which it will be found, its distance from the hive and its quality rating (or
fitness). This information guides the bees to find the flower patches
precisely, without the use of guides or maps. Each individual's knowledge
of the outside environment is gleaned solely from the waggle dance. This
dance enables the colony to evaluate the relative merit of different patches
according to both the quality of the food they provide and the amount of
energy needed to harvest it.
To identify the optimal location of bio-mass power plant
(David et al 2010), Resource Allocation (Nicanor and Kevinl 2010),
Continuous Optimization Problem (Hai Bin et al 2010), Constraint
Optimization Problem (Dervis et al 2011), Economic power dispatch
(Rajesh et al 2011), data Clustering in data mining (Mohammad et al
2007) (Dervis and Celal 2011) (Changsheng et al 2010), and Path
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management in the computer network are some of the successful
solutions based on ABC algorithm. The detailed honey bee mating
algorithm is explained below.
4.2. RECENT RESEARCH IN ABC
The survey on ABC which includes the detailed problem
description, implementation and comparison with its counterpart is
described in Dervis and Bahriye (2009), Taher et al (2010), Dervis and
Celal (2011), Hongnian et al (2010). In which Hongnian et al (2010),
Dervis and Bahriye (2009) described the biological nature of honey bee
and its colony behaviour. And this work described the study of bionics
bridges with the engineering functions, biological structures of animals
and insects, and organizational principles found in the nature which
mapping with the modern technologies.
Michelle and Stephen (2005) explained the numerous
mathematical definitions and compared the implementation of ABC with
other exiting meta-heuristic algorithms. This work explains the
knowledge transferring process from the life forms to the human modern
technologies. The output of bionics study of ABC includes not only
physical products, whereas also various computation methods that can be
applied in different areas.
The authors reviewed the various nature-inspired algorithms
such as ACO, ABC, Genetic Algorithm (GA), and Fire-Flies (FF)
Algorithm and concluded that the nature-inspired algorithms could
hybridize together with other algorithms to enhance it to be faster, more
efficient, and more robust.
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The ABC algorithm was first proposed for unconstrained
optimization problems where it showed superior performance. Dervis and
Celal (2011) describes a modified ABC algorithm for constrained
optimization problems and compares the performance of the modified
ABC algorithm against those of state-of-the-art algorithms for a set of
constrained test problems.
consisting of three simple heuristic rules and a probabilistic selection
scheme for feasible solutions based on their fitness values and unviable
solutions based on their violation values. Structural optimization,
engineering design, economics and resource allocation are just a few
examples of fields for constrained optimization problems.
The authors implemented the ABC with various benchmark
functions and compared with Particle Swarm Optimization (PSO),
Differential Evaluation Algorithm, and GA. The author concluded that the
performance of ABC algorithm is better than the other algorithms even
though it uses less control parameters and it can be efficiently used for
solving multimodal and multidimensional optimization problems.
Jun Zhao et al (2011) further extended the ABC for parallel
computing problem; the parallel computing provides efficient solutions
for combinatorial optimization problem. Parallel computing can shorten
the time to give a solution. Therefore greater attention has been made by
the researchers towards parallel computation. However, the actual parallel
or distributed algorithm is generally based on the real devices of computer
cluster or multi-core processor.
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The ABC has already applied in various engineering problem
and established optimal performance than existing algorithms which
includes clustering in data mining (Changsheng et al 2010), Travelling
Salesman Problem (Peibo and Huaxi 2010), Economic power dispatch
(Rajesh et al 2011), resource allocation (Nicanor and Kevin 2010),
optimal location computation (David et al 2010) and for vehicular routing
(Yannis and Magdalene 2010).
Nicanor and Kevin (2010) illustrated the practical utility of the
theoretical results and algorithm of honey bee algorithm, and show that
how it can solve a dynamic voltage allocation problem to achieve a
maximum uniformly elevated temperature in an interconnected grid of
temperature zones. In Jiejin et al (2010), the authors proposed a novel
hybrid ABC and Quantum Evolutionary Algorithm for solving continuous
optimization problems. ABC is adopted to increase the local search
capacity as well as the randomness of the populations.
These implementations have been tested on several well-known
real datasets and compared with other popular heuristics algorithms such
as Genetic Algorithm (GA), Simulated Annealing (SA), Tabu Search
(TS), ACO and the recently proposed algorithms like improved PSO.
The computational simulations reveal very encouraging results
in terms of the quality of solution and the processing time required
Honey-bees are among the most closely studied social insets. Their
foraging behaviour, learning, memorizing and information sharing
characteristics have recently been one of the most interesting research
areas in swarm intelligence.
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Rajesh et al (2011) presented a new multi-agent based hybrid
particle swarm optimization technique applied to the economic power
dispatch. The earlier PSO suffers from tuning of variables, randomness
and uniqueness of solution. The algorithm integrates the deterministic
search, the Multi-agent system, the PSO algorithm and the bee decision-
making process. The economic power dispatch problem is a non-linear
constrained optimization problem.
Classical optimization techniques like direct search and
gradient methods fails to give the global optimum solution. Other
Evolutionary algorithms provide only a good enough solution. To show
the capability, the author has applied to two cases 13 and 40 generators,
respectively. The results show that this algorithm is more accurate and
robust in finding the global optimum than other.
David et al (2010) discussed a new calculation tool based on
particles swarm which named as Binary Honey Bee Foraging (BHBF).
Effectively, this approach has made it possible to determine the optimal
location, biomass supply area and power plant size that offer the best
profitability for investor. Moreover, it prevents the accurate method,
which may not be feasible from computational viewpoint. In this work,
Profitability Index (PI) is set as the fitness function for the BHBF
approach.
Changsheng (2011) proposed a clustering approach for
optimally partitioning of N objects into K clusters. The author tested the
proposed system with several well-known real datasets and concluded
that the ABC performs well than other popular heuristics algorithm in
clustering, such as GA, PSO, Scatter Search (SS), TS, and ACO. The
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result of all above proposals shows that the performance of honey bee
algorithm is optimal than other existing algorithms.
Alok (2009) and Michael et al (2010) applied the ABC in the
studies of computer science and engineering for network routing and
minimum spanning tree. Alok (2009) designed and implemented the ABC
for leaf-constrained minimum spanning tree problem and concluded that
computation time in the ABC is quite small and it completely
outperforms both in terms of solution quality as well as running time. The
author proposes ABC based solution for the given an undirected,
connected, weighted graph, the leaf-constrained minimum spanning tree
problem.
This work seeks on this graph a spanning tree of minimum
weight among all the spanning trees of the graph that have at least number
of leaves. This work differs from other implementations in the following
features: In existing implementation, if the solution associated with an
employed bee does not improve for a predetermined number of iterations
then it becomes a scout bee. Second possibility of a scout bee may be due
to collision.
There are no limits on the number of scouts in a single iteration
like other ABC algorithms. Also number of scouts depend on the above
two conditions. There can be many scouts in the iteration if these two
conditions are satisfied many times, or there can be no scout if these two
conditions remain unsatisfied.
Michael et al (2010) made a detailed review of bio-inspired
routing algorithm such that ABC and ACO. The author discusses in some
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depth why biology is an appealing and appropriate place to find
inspiration for computer networking research.
The work covers a review on routing research inspired by the
behaviour of social insects, intrusion and misbehavior detection research
inspired by the immune system, network services modeled on the
interactions and evolution of populations of organisms, research that
applies techniques from the field of epidemiology, and presents a
sampling of newly emerging bio-inspired research topics.
It is observed that the performance of ABC may be further
improved by 1) optimal value assignment for the constants, which was
assumed for almost all the previous work, and 2) the initial number of
scout bee, if this is not optimally selected then there are many chances for
local optimal (zero-to-infinity) problem.
4.3. PROPOSED ABC BASED WIRELESS CLUSTERING
The proposed Artificial Bee Colony Based Wireless Clustering
(ABCWC) requires a number of parameters to be set, namely:
1. number of scout bees (n),
2. number of elite bees (e),
3. number of patches selected out of n visited points (m),
4. number of bees recruited for patches visited by "elite bees"
(nep),
5. number of bees recruited for the other (m-e) selected patches
(nsp),
6. size of patches (ngh) and
7. stopping criterion.
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4.3.1 Electing Efficient Cluster Head using ABC Algorithm
The algorithm starts with the n scout bees being placed
randomly in the search space.
The bees search for food sources in a way that maximizes the
ratio, the energy function is shown in the following equation (4.1)
T
E)(F
(4.1)
Where, E is the energy obtained, and T is the time spent for
foraging. Here E is proportional to the nectar amount of food sources.
In a maximization problem, the goal is to find the maximum of
RP. R
P represents the region of search
area.
i is the position of the ith
i)
i and it is
i).
i(C) | i = 1, 2... S} represent the population of
food sources being visited by bees, in which, C is cycle, and S is number
of food sources around the hive. The preference of a food source by the
nectar amount of the food source increases, the probability with the
preferred source by the worker bee increases proportionally. Therefore,
i will be chosen by a bee
can be expressed in equation (4.2)
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s
1k
k
i
i
)(F
)(FP
(4.2)
The position of the selected neighbour food source is
calculated as the following equation (4.3) and (4.4),
)C()1C(ii (4.3)
and the stop criteria of the system is
thiiH)E(N)Q(N (4.4)
where,
Ni (Q) represents the values of nectar of Queen,
Ni (E) represents the values of nectar of Elite bee, and Hth
represents the minimum threshold value of the Hive.
At the end of iteration, the colony will have two parts to its
new population - representatives from each selected patch and other scout
bees assigned to conduct random searches.
4.3.2 Pseudo-code of ABC Algorithm
Initialization
Generate the initial population of the bees
Selection of the best bee as the queen
Selection of the maximum number of mating flights (n)
Main Phase
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do while i n
Initialize queen spermatheca, energy and speed.
do while energy > threshold and spermatheca is not full
Select a drone
if the drone passes the probabilistic condition then
Add sperm of the drone in the spermatheca
endif
Update Speed
Update Energy
enddo
do j = 1, Size of Spermatheca
Select a sperm from the spermatheca
Generate a brood by applying a crossover operator between the queen, the
selected drones and the adaptive memory
Select, randomly, a worker
if the brood then
Replace the queen with the brood
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Else
if
Replace the drone with the brood
endif
endif
enddo
enddo
return The Queen (Best Solution Found)
A sample wireless adhoc network is shown in the Fig.4.1. The
sample nectar (energy / battery power) of each node is shown in the
Fig.4.1 is listed in the Table 4.1. The energy function is applied to fig.4.1
based on the values shown in table 4.1, which is recorded in table 4.2.
As the first step, the hive generates 16 scout bees which is
simply a hello message in the network terminology. These bees fly which
is simply flooding of hello message, into the region of food source
(wireless network). These all scout bee access any one food source
(flower) and collect the nectar (energy).
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11
10
8
9
7
6
5 4
3
2
12
1
15
14
13
H
16
Fig.4.1 Sample Node Deployment
Table 4.1 Node Details of Figure 4.1
Flower (Node) Nectar (Energy)
1 90
2 75
3 80
4 40
5 50
6 60
7 90
8 75
9 80
10 40
11 76
12 60
13 45
14 75
15 80
16 40
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Table 4 i) of Figure 4.1
Node Energy IMP i)
1 90 4 23
2 75 3 25
3 80 4 20
4 40 5 8
5 50 4 13
6 60 3 20
7 90 3 30
8 75 4 19
9 80 5 16
10 40 4 20
11 76 3 25
12 60 2 30
13 45 2 23
14 75 1 75
15 80 1 80
16 40 1 40
At the 8th
time unit (refer the Table 4.2), the dance of
scout_bee_4 in the food_source_4 is elapsed, here for the simple
neighbouring bee, here scout_bee_1, 3, 5 in the food_source_1, 3, 5 is
dancing with the rhythm values of 23, 20 and 13 respectively, so the
scout_bee_4 enter into food_source_1. When the guest bee (scout_bee_4)
enters, the scout_bee_1 will update its guest nectar table, which is shown
in the Table 4.3.
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Table 4.3 Nectar Table of Scout_bee2
Food Source ID Nectar Ratio F
1 8
Then it flies to the hive with its own nectar (routing) table.
2nghb (4.5)
From the given example, at 23rd
time units as indication in
the table 4.4, all the neighbouring bees appeared in the dancing floor of
scout_bee2 with the value of (3,20),(5,13),(1,23),(4,8),(6,20), where the
first value indicates the food source id and the second value indicates the
nectar value of the concerned food source.
Now there are 5 bees appeared, so the scout_bee_2 is elected
as the elite bee of the patch (consider this as patch 1). The nectar table of
patch1 is shown in the Table 4.4.
Table 4.4 Nectar Table of Elite Bee in the Patch1
Food Source ID Nectar Ratio F
1 23
3 20
4 8
5 13
6 20
In the hive, elite bee from patch1, patch2 and patch3 is
reached, and then the Elite Bee and Patch Routing table of Hive is
formed, which is shown in the Tables 4.5 and 4.6 respectively.
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Table 4.5 Elite bee Table in the Hive
Food Source ID Patch ID
2 1
7 2
15 3
Table 4.6 Patch Routing Table in the Hive
Food Source ID Next Hop Patch ID
1 2 1
2 * 1
3 2 1
4 2 1
5 2 1
6 2 1
7 * 2
8 7 2
9 7 2
10 7 2
11 7 2
12 15 3
13 15 3
14 15 3
15 * 3
16 15 3
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In the
and the number indicates the id of the concern elite bee.
The bio-logical terms and its corresponding network
terminology are mapped in the Table 4.7.
Table 4.7 Mapping of Biological Terminology with
Network Terminology
Bio-logical terms Network Routing
Bee Hello message
Food Source (or Flower) Node
Nectar Energy / Power
Nectar (or Patch) Table Routing Table
Waggling Dance Waiting Time
Elite Site Cluster Head
Hive Control Station (Real /Imaginary Node)
4.4 RESULTS AND PERFORMANCE ANALYSIS
Figure 4.2 Design of Type1 Wireless Network
WL1 WL2
WL5 WL6
WL8 WL7
WL4 WL3
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The routing protocol defined in the implementation (shown in
the tables and in the figures) is the advancement of the latest proposal
over the traditional routing protocol, AODV (Yang et al 2011). The
average response time which shown in the tables 4.9 and fig.4.3 are the
combination of route discovery time, transmission time, propagation
delay in each node, and waiting time in the intermediate queue.
The proposed ABCWC is implemented in Network Simulator
2 (NS2). In the huge wireless routing, the AODV is prominent routing
protocol (Kalwar, 2010) which modified by many researchers in the past
few decades. In which, the neighbour detection for AODV (Krco et al,
2003), improving efficiency of AODV, combination of AODV with DSR
(Bai et al, 2006), secured AODV (Cerri et al, 2008), dynamic anomaly
detection (Nakayama et al, 2009), Route Recovery mechanism (Pereira et
al, 2010) are remarkable work. The proposed work is compared with
recent AODV routing in cluster environment which is proposed by
Pereira et al (2010).
The performance is tested in a variety of nodes on wireless
network, and using various transport protocol on UDP. Figure 4.2 shows
the design of type 1 wireless network and Table 4.8 shows the various
types of wireless network used for the simulations. The simulation is
implemented for 10 seconds. The throughput, response time and packet
loss are calculated for entire 10 seconds and the mean value of each
calculation is shown in the following Table 4.9, 4.10, 4.11.
The average response time shown in the Table 4.9 and fig.4.3
are the combination of route discovery time, transmission time,
propagation delay in each node, and waiting time in the intermediate
queue. The average response time and throughput in wireless
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environment are recorded in the Table 4.9 and 4.10. The average response
time and the throughput of proposed work are improved in the present
work. This causes improvement in the throughput of proposed ABCWC,
which reflects in the Table 4.10.
Table 4.8 Design Details of Wireless Network
Type of
network
No of
nodes
No of
cluster
No of Nodes in
each cluster
No of mobile
Nodes
Type 1 8 2 4 6
Type 2 20 4 5 18
Type 3 50 10 5 27
Type 4 75 5 15 24
Type 5 100 5 20 24
Type 6 200 10 20 27
Table 4.9 RTT in Wireless Routing
No of nodes No of cluster No of Nodes in each
cluster AODV
Proposed
ABCWC
10 2 5 178 168
20 4 5 196 178
40 8 5 213 189
100 20 5 242 214
200 20 10 288 254
500 25 20 345 297
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Table 4.10 Throughput in Wireless Routing
No of nodes AODV Proposed ABCWC
10 187.2 193.5
20 198.7 206.4
40 212.1 229.9
100 278.2 294.2
200 389.4 411.9
500 467.9 496.7
Table 4.11 Packet Loss of proposed and existing routing protocols
No of Nodes AODV Proposed ABCWC
10 6 1
20 12 2
40 19 4
100 28 6
200 37 16
500 49 27
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Fig.4.3 Comparison of Round Trip Time
Fig.4.4 Comparison of Throughput
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Fig.4.5 Comparison of Packet Loss
Fig.4.6 Packet Loss in proposed work with trend line
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Fig.4.7 Throughput of proposed work with trend line
Fig.4.8 Round Trip Time in proposed work with trend line
Fig4.3 seems the performance of proposed ABCWC is
improved one than the existing AODV as a minimum of 6% and as a
maximum of 14% in RTT. The performance of proposed ABCWC is
improved one than existing AODV as a minimum of 3% and as a
maximum of 8% in Throughput.
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Fig4.4 seems the throughput of ABCWC is improved around
5kbps than the existing routing protocol (Pereira et al, 2010) and a
consequence the proposed work reduces the average response time (from
2ms to 3ms). As the result of reduced response time the number of packet
travelled in a unit time is increased.
There are no packet losses in the proposed work at lesser no.
of nodes and packet losses on higher no. of nodes are reduced
dramatically then existing AODV protocols.
The proposed ABCWC reduces response time in UDP. The
average response time shows transmission rate of the network, which
leads to the number of packet travelled in a unit time, is increased. This
efficient data transfer is visualized in the throughput. Therefore, the
proposed ABCWC provides efficient data transmission on wireless
network, hence it is concluded that ABCWC is an efficient routing
protocol than existing system.