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Transcript of 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