Overview of SWARM INTELLIGENCE and ANT COLONY OPTIMIZATION

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Overview of SWARM INTELLIGENCE and ANT COLONY
OPTIMIZATION
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SWARM INTELLIGENCE

• Based on social interactions (locally shared
  knowledge) that provides the basis for unguided
  problem solving.
• Efficiency is related to the degree of
  connectedness of the network and the number of
  interacting agents.

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CHARACTERISTICS OF SWARM

• Distributed, no central control
• Limited communication
• No explicit model of environment
• Perception of the environment
• Composed of many, alike individual agents.

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Examples

• Ant colony optimization
• River formation dynamics
• Particle swarm optimization
• Gravitational search algorithm
• Intelligent water drops

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ANT COLONY OPTIMIZATION

• Developed by M.Dorgio in 1992
• Heuristic optimization method inspired by the
  observation of real ant colonies.
• Based on how ants find the shortest path
• to food source.
• The behavior of ants is a kind of stochastic
  distributed optimization behavior.

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BEHAVIOR OF REAL ANTS

• Ants are blind, deaf and dumb.
• So how do they find the shortest path to
• food sources?
• Based on PHEROMONES.
• They follow the deposits of pheromones and
• form a trail.
• Other ants get attracted to this trail.
• Pheromones are volatile in nature.

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CONTD

• Each ant choose an action based on
• Random choice
• Pheromone mediated
• They move by sensing previous ant not by sensing
  the environment.
• Each ant collects info about local
• environment and act concurrently and
  independently.
• Stigmergy governs info exchange.

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APPLICATIONS

• Network routing
• Travelling sales man problem
• Vehicle routing
• Assignment problems
• Set problems

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