Lecture 8 AIM

8/18/2019 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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&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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'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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• 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.


7/29CE00371-1: Introduction to Software Deelopment !ro"lem Soling u#ing programmed #olution#CT002-3.5-2 AI-Methods

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



• 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



+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.



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.



• (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.



*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.



*+C *lgorithm



+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.



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.



• $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.



Ant foraging

Cooperatie #earch " pheromone trail#


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CE00371-1: Introduction to Software Deelopment !ro"lem Soling u#ing programmed #olution#CT002-3.5-2 AI-Methods

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*

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Lecture 8 AIM