Ritesh Go El

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Swarm Intelligence

RITESH GOEL

reg.no:0901341154

sic no:09et0150

Guided by:Er. Aditya hota

03-09-2012


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Swarming The Definition:

aggregation of similar animals,

generally cruising in the same

direction

Termites swarm to build colonies

Birds/ant swarm to find food

Bees swarm to reproduce


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Swarming :scientist observations


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Observations:

Ants find shortest path to food source

from nest.

Ants deposit pheromone along travelledpath which is used by other ants to follow

the same path

Simple rules for each individual. No central control.

Individuals are benefited from the group.


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So, what about intelligence?

Social behaviour increases the ability of anindividual to adapt.

Intelligence arises from interactions amongindividuals.

From the swarming observations of ant,swarming intelligence is developed which isutilized for optimization technique(mostly).


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NEED FOR NEW TECHNIQUIES

For reducing the complexity.

To design system self-organised.

Traditional software and hardware is not efficient.

For salesman problem.


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SWARM INTELLIGENCE:INTRODUCTION

Any attempt to design algorithms or distributed

problem solving devices inspired by the swarming

behavior of animals is known as swarm intelligence

or

swarm intelligence is an artificial intelligence

technique based on the study of decentralized ,self-

organised systems


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HISTORY:

Introduced by Gerardo beni and Jing wang in 1989.In the context of cellular robotic system.

yearly international conference

eg:-ANTS 2002-from ant colony to

artificial ants.


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PROPERTIES

It is composed of many indivisuals.

Indivisuals are relatively homogeneous.

No central control

Groups Increases self-organisation.

simple rules for indivisuals.


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ALGORITHMS OF SWARM INTELLIGENCE:

Ant colony optimization(ACO)

River formation dynamics

Particle swarm optimization(PSO)

Stochastic diffusion search

Gravitational search algorithm(GSA)


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Continued,,,,

Intelligent Water Drops

Charged System Search


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ANT COLONY OPTIMIZATION(ACO)

Based on actions of ant colony.

Introduced by Macro Dorigo.(in 1992)

Useful in solving Discrete optimization

problem.(salesman and shortest path problem)


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ACO: in satellite network


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Travelling salesman problem


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PARTICLE SWARM OPTIMIZATION

Population based optimization Technique

Developed by Dr. Eberhart and Dr. kennedy in 1995.

Based on bird flocking or fish schooling.

simples rules for the indivisual


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eg: PSO

Crowd Simulation:

lord of rings

Airlines uses swarm theory to simulate

passengers boarding a plane.

Wavelet based video coding.

Android applications


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RIVER FORMATION DYNAMICS

Gradient version of ACO

It describes how river is formed.

water transform the environment ,the altitudes ofthe places are modified and decreasing gradients

are created.

this gradients are followed by subsequent dropsand created new gradients.


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GRAVITATIONAL SEARCH ALGORITHM

Constructed based on law of gravity and mass

interactions.

It uses newtonion physics

every mass see the situations of different

masses.

transeffering of information between two

masses through gravitational force.


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APPLICATIONS: SWARM INTELLIGENCE

By US military for contolling vechiles.

VLSI floor planning is based on pso.

In physical circuit design it is used to reduced the

cuts.

video and image processing Applications.


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IN NEAR FUTURE

NASA is investigating the use of swarm

technology for planetary mapping.

European Space Agency is thinking about an

orbital swarm for self assembly and

interferometer.


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ADVANTAGES

EVOLUTIONS

Ability to adjust to new situations.

highly adoptable.chance of failure is minimal.


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DISADVANTAGES

Non-optimal and uncontrollable

Unpridictable.

Non understable(rarely)


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CONCLUSION


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References

www.fadooengineers.com

Ritesh Go El

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