Particle Swarm Optimization

Stigmergy is associated with two words stigma and ergon (stigma (sting) + ergon (work) = stimulation by work). Stig-mergy is based on the principle that, an environment serves as a work state memory where work does not depend on specific agents[7].

When an optimization problem, modeling a physical sys-tem, involves only one objective function, the task of finding the lone optimal solution is called single-objective optimization and the problem is called single-objective optimization prob-lem (SOP). A multi-objective optimization problem (MOP) deals with more than one objective functions. Particle Swarm Optimization (PSO) is a heuristic search method that simulates the movements of a flock of birds which aim to find food. The relative simplicity

A number of basic variations have been developed due to improve speed of convergence and quality of solution found by the PSO[9].

Kennedy and Eberhart, considering the behavior of swarms in the nature, such as birds, fish, etc. developed the PSO algorithm. The PSO has particles driven from natural swarms with communications based on evolutionary computations. PSO combines self-experiences with social experiences. In this Algorithm, a candidate solution is presented as a particle. It uses a collection of flying particles (changing solutions) in a search area (current and possible solutions) as well as the movement towards a promising area in order to get to a global

This algorithm utilizes the behaviour of the real ants while searching for the food. It has been observed that the ants deposit a certain amount of pheromone in its path while traveling from its nest to the food. Again while returning, the ants are subjected to follow the same path marked by the pheromone deposit and again deposit the pheromone in its path. In this way the ants following the shorter path are expected to return earlier and hence increase the amount of pheromone deposit in its path at a faster rate than the ants following a longer