AIT101 MICROBIOLOGY Lecture: Yeast Kamonchai Cha-aim, Ph.D. February 9th, 2015.
Lecture 8 AIM
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CT002-3-2 AI Methods
Swarm Intelligence, technique
and application-II
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CE00371-1: Introduction to Software Deelopment !ro"lem Soling u#ing programmed #olution#
CT002-3.5-2 AI-Methods
$earning %utcome#
• Details understand on Swarm concept
a! Sel" or#ani$ation
%! Di&ision o" la%or
c! 'eproduction
d! (ora#in#
e! etc
• To discuss A)C al#orithm
• Stud* o" Stimer#* in S+A'M intelli#ence
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CE00371-1: Introduction to Software Deelopment !ro"lem Soling u#ing programmed #olution#
CT002-3.5-2 AI-Methods
&ow 'o 'hin( Swarm Intelligence
%"#eration
%"#eration )odel )etaheuri#tic
Simulation *lgorithm
+
u i l d
'
e # t
EtractCreate
efine
Swarm intelligence .SI/ as de"ined %* )ona %eau, Dori#oand Theraula$ is "any attempt to design algorithms or
distributed problem-solving devices inspired by the
collective behavior of social insect colonies and other
animal societies"
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CT002-3.5-2 AI-Methods
'e*nolds created a %old model in /1 - A distri%uted%eha&ioral model, to simulates the motion o" a "loc o"
%irds.
ach "old is an independent actor that na&i#ates on itsown perception o" the d*namic en&ironment.
Four Rules of Bold Model
A&oidance ruleCop* ruleCenter rule4iew rule
Modeling
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What are there principal mechanisms of
natural organization?
Self-organiation
‘Self-organization is a set of dynamical mechanisms
whereby structures appear at the global level of a system
from interactions of its lower-level components.’
(Bonabeau et al, in Swarm Intelligence, 1999
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CT002-3.5-2 AI-Methods
• Self-organization : can be defined as a set of dynamical
mechanisms that establish basic rules for interactions between
the components of the system.
• The rules ensure that the interactions are executed on the basisof purely local information without any relation to the global
pattern.
• Division of Labor: In swarm behavior different tasks are performed simultaneously by specialized individuals which is
referred to as division of labor. It enables swarm to respond to
changed conditions in the search space.
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a)ositive feedback !amplification)
b) "egative feedback !for counter#balance and stabilization)
c)$mplification of fluctuations !randomness% errors% random walks)
d)&ultiple interactions
'he four "a#e# of #elf-organiation
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• Positive feedback: $s the nectar amount of food sources
increases% the number of onlookers visiting them increases%
too.
• Negative feedback: The exploitation process of poor food
sources is stopped by bees.
• Fluctuations: The scouts carry out a random search processfor discovering new food sources.
• Multiple interactions: 'ees share their information about
food sources with their nest mates on the dance area.
Self-organiation
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+ehaior of &one +ee Swarm
Three essential components o" "ora#e selection
• 2ood Source#: The &alue o" a "ood source depends on man* "actors such as its
proimit* to the nest, its richness or concentration o" its ener#*, and the ease o"
etractin# this ener#*.
• Emploed 2orager#: The* are associated with a particular "ood source which
the* are currentl* eploitin# or are 6emplo*ed7 at. The* carr* with them in"ormationa%out this particular source, its distance and direction "rom the nest, the
pro"ita%ilit* o" the source and share this in"ormation with a certain pro%a%ilit*.
• nemploed 2orager#: The* are continuall* at loo out "or a "ood source to
eploit. There are two t*pes o" unemplo*ed "ora#ers scouts, searchin# the
en&ironment surroundin# the nest "or new "ood sources and onlooers waitin# inthe nest and esta%lishin# a "ood source throu#h the in"ormation shared %*
emplo*ed "ora#ers.
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Echange of Information among "ee#
• The echan#e o" in"ormation amon# %ees is the most
important occurrence in the "ormation o" collecti&e
nowled#e.
• The most important part o" the hi&e with respect to
echan#in# in"ormation is the dancin# area
• Communication amon# %ees related to the 8ualit* o"
"ood sources taes place in the dancin# area.
• This dance is called a Waggle dance.
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• (mployed foragers share their information with a probability
proportional to the profitability of the food source% and the sharing
of this information through waggle dancing is longer in duration.
• $n onlooker on the dance floor% probably she can watch numerous
dances and decides to employ herself at the most profitable
source.
• There is a greater probability of onlookers choosing more profitable sources since more information is circulated about the
more profitable sources.
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*rtificial +ee Colon *lgortihm
• imulates behavior of real bees for solving multidimensional andmultimodal optimisation problems.
•The colony of artificial bees consists of three groups of bees:employed bees% onlookers and scouts.
• The first half of the colony consists of the employed artificial beesand the second half includes the onlookers.
• The number of employed bees is e*ual to the number of foodsources around the hive.
• The employed bee whose food source has been exhausted by the
bees becomes a scout.
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*+C *lgorithm
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+a#ic Self %rganiation !ropertie#
• 9ositi&e "eed%ac As the nectar amount o" "oodsources increases, the num%er o" onlooers &isitin#them increases, too.
• :e#ati&e "eed%ac The eploitation process o" poor"ood sources is stopped %* %ees.
• (luctuations The scouts carr* out a random search
process "or disco&erin# new "ood sources.
• Multiple interactions )ees share their in"ormationa%out "ood sources with their nest mates on the dancearea.
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Eplanation
• (ach cycle of search consists of three steps: moving theemployed and onlooker bees onto the food sources and
calculating their nectar amounts+ and determining the scout
bees and directing them onto possible food sources.
• $ food source position represents a possible solution to the
problem to be optimized.
• The amount of nectar of a food source corresponds to the*uality of the solution
• ,nlookers are placed on the food sources by using a
probability based selection process.
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• $s the nectar amount of a food source increases% the
probability value with which the food source is preferred byonlookers increases% too.
• The scouts are characterized by low search costs and a low
average in food source *uality. ,ne bee is selected as the scout bee.
• The selection is controlled by a control parameter called
-limit-.
• If a solution representing a food source is not improved by a
predetermined number of trials% then that food source is
abandoned and the employed bee is converted to a scout.
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Ant foraging
Cooperatie #earch " pheromone trail#
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Structure emerging from a homogeneou# #tartup #tate4
)ulti#ta"ilit - coei#tence of man #ta"le #tate#4 State tran#ition# with a dramaticall change of the
##tem "ehaiour4
Characteri#tic# of #elf-organied
##tem#
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Self-organiation in a termite #imulation
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Stigmerg: stigma (sting ! ergon (wor
5 6#timulation " wor(
Characteri#tic# of #tigmerg
Indirect a#ent interaction modi"ication o" the en&ironment n&ironmental modi"ication ser&es as eternal memor* +or can %e continued %* an* indi&idual
The same, simple, %eha&ioural rules can create di""erentdesi#ns
Accordin# to the en&ironmental state
Stigmerg
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2rom *nt# to *lgorithm#
• Swarm intelli#ence in"ormation allows us to address
modelin# &ia
; 9ro%lem sol&in# ; Al#orithms
; 'eal world applications
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)odeling
• %"#ere !henomenon
• Create a "iologicall motiated model
• Eplore model without con#traint#
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)odeling444
• Create# a #implified picture of realit
• %"#era"le releant quantitie#
"ecome aria"le# of the model
• %ther .hidden/ aria"le# "uild
connection#
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* 8ood )odel ha#444
• !ar#imon .#implicit/
• Coherence
• efuta"ilit
• !arameter alue# corre#pond toalue# of their natural counterpart#
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'raelling Sale#per#on
!ro"lemInitiali$e
$oop 9 at thi# leel each loop i# called an iteration 9
ach ant is positioned on a startin# node
$oop 9 at thi# leel each loop i# called a #tep 9
ach ant applies a state transition rule to incrementall*
%uild a solution and a local pheromone updatin# rule
ntil all ants ha&e %uilt a complete solution
A #lo%al pheromone updatin# rule is appliedntil nd
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'raeling Sale# *nt#
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; < *
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=et 'opic
*rtificial Immune S#tem I• B&er&iew o" AIS
• nderstand %ac#round o" immunolo#*
• 'oles o" AIS
• Immune pattern reco#nition
• Immune networ theor*