Overview of SWARM INTELLIGENCE and ANT COLONY OPTIMIZATION

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.

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

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

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.

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.

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.

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