1. Overview of SWARM INTELLIGENCE and ANT COLONY OPTIMIZATION

2. 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.

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

4. Examples Ant colony optimization River formation dynamics
Particle swarm optimization
Gravitational search algorithm
Intelligent water drops

5. 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.

6. 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.

8. 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.

9. APPLICATIONS Network routing Travelling sales man problem
Vehicle routing
Assignment problems
Set problems