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SWARM INTELLIGENCESWARM INTELLIGENCEFrom Natural to Artificial Systems



OVER VIEWOVER VIEWy Introduction

y History

y Algorithms

y Taxonomyy Characteristics

y Applications

y

Scopey Limitations

y Recap



INTR ODUCTIONINTR ODUCTION

y Swarm intelligence (SI) describes the

collective behaviour of decentralized, self-

organized systems, natural or artificial.

y The concept is employed in work on

artificial intelligence.



SISI -- DEFINITIONDEFINITION

³any attempt to design algorithms or distributed problem-solving devices inspired by the collective

behaviour of social insect colonies and other

animal societies ́ [Bonabeau, Dorigo,

Theraulaz: Swarm Intelligence]



BRIEF HISTORYOF SIBRIEF HISTORYOF SI

y One of the first researchers in this field

was French biologist Grassé.

y In 1970s ARPA financed research projects

on the first multi-agent systems with the

Hearsay-II blackboard project.



HISTORY CONTD«.HISTORY CONTD«.

y In this decade the ³Actor Model´ was

invented by Carl Hewitt, Peter Bishop and

Richard Steiger.

y The Multi-Agent Systems Lab at Amherst

held the first workshop on ³distributed

artificial intelligence´ in 1980.



SWARMINGSWARMING ± ± EXAMPLEEXAMPLE

y Bird Flocking.

y ³Boids

´model was proposed by Reynolds

Boids = Bird-oids (bird like)

y Only three simple rules .



COLLISION

AVOIDANCE

FLOCK CENTERING

VELOCITY

MATCHING



Collision AvoidanceCollision Avoidance

y Rule 1: Avoid Collision with neighbouring

birds .



Flock CenteringFlock Centering

y Rule 2: Stay near neighbouring birds.



Velocity MatchingVelocity Matching

y Rule 3: Match the velocity of

neighbouring birds.



EXAMPLE ALGORITHMSEXAMPLE ALGORITHMS

1. Ant Colony O ptimization.

2. Particle Swarm O ptimization.3. Stochastic Diffusion Search.

4. Gravitational Search Algorithm.



1.1. Ant Colony OptimizationAnt Colony Optimization --

Biological InspirationBiological Inspiration

y Inspired by foraging behaviour of ants.

y Ants find shortest path to food source

from nest.

y Ants deposit pheromone along travelled

path.

y Has adaptability,robustness and

redundancy.



Foraging behavior of AntsForaging behavior of Ants

2 ants start with equal probability of going

on either path.




The ant on shorter path has a shorter to-

and-fro time from it¶s nest to the food.




The density of pheromone on the shorter path is

higher because of 2 passes by the ant (as compared to

1 by the other).




The next ant takes the shorter route.




Over many iterations, more ants begin using the path with higher pheromone, thereby further

reinforcing it.




After some time, the shorter path is almost

exclusively used.



Ant Colony MetaheuristicAnt Colony Metaheuristic



2. Particle Swarm Optimization2. Particle Swarm Optimization

Bird flocking is one of the best example of

PSO in nature.One motive of the development of PSO was

to model human social behavior.



Particle SwarmOptimizationParticle SwarmOptimization

y In particle swarm optimization (PSO), a

set of software agents called particle

search for good solutions.

y Each particle uses its own experience and

the experience of neighbour particles tochoose how to move in the search space.



3.Stochastic diffusion search3.Stochastic diffusion search

y This type of swarm intelligence is based

on the tendem-calling mechanism used by

a variety of ants.

y Individual agents update their own

preferences while randomly testing new

hypotheses.

y This process culminates in a collective or sub-collective choice of the optimal

solution.



4. Gravitational Search Algorithm4. Gravitational Search Algorithm

y GSA is constructed based on the law of

Gravity and the notion of mass

interactions.

y The GSA algorithm uses the theory of

Newtonian physics and its searcher agentsare the collection of masses.



TAXONOMYOF SITAXONOMYOF SI

y Natural vs. Artificial.

yScientific vs. Engineering.



SWARMINGSWARMING

CHARACTERISTICSCHARACTERISTICS

y Simple rules for each individual.

y No central control.

Decentralized and hence robust.

y Emergent.

Performs complex functions.



APPLICATIONS OF SIAPPLICATIONS OF SI

y Crowd simulation.

y Ant-based routing.

y

Swarm Robotics.y creation of the video sequence.



SCOPE OF SISCOPE OF SI

y The U.S. military is investigating swarm

techniques for controlling unmanned

vehicles.

y The European Space Agency is thinking

about an orbital swarm for self assemblyand interferometry.



SCOPE CONTD«.SCOPE CONTD«.

y A 1992 paper by M. Anthony Lewis and

George A. Bekey discusses the possibility

of using swarm intelligence to control

nanobots within the body for the purpose

of killing cancer tumors.

y NASA is investigating the use of swarmtechnology for planetary mapping.



SISI -- LIMITATIONSLIMITATIONS

y Theoretical analysis is difficult, due to

sequences of probabilistic choices.

y Most of the researches are experimental.

y Though convergence is guaranteed, time

to convergence is uncertain.



RECAPRECAP

y Swarm intelligence is the discipline that

deals with natural and artificial systems

composed of many individuals that

coordinate using decentralized control andself-organization.

y Provide heuristic to solve difficult

problems .y Has been applied to wide variety of

applications.



REFERENCESREFERENCESy Reynolds, C. W. (1987) Flocks, Herds, and Schools: A

Distributed Behavioral Model, in Computer Graphics,21(4) (SIGGRAPH '87 Conference Proceedings) pages 25-34.

y

James Kennedy, Russell Eberhart. Particle SwarmO ptimization, IEEE Conf. on Neural networks ± 1995

y www.adaptiveview.com/articles/ ipsop1

y

M.Dorigo, M.Birattari, T.Stutzle, Ant colonyoptimization ± Artificial Ants as a computationalintelligence technique, IEEE ComputationalIntelligence Magazine 2006



.

D umb parts,

properly connected into

a swarm

yield smart results



THANK YOUTHANK YOU

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