A Particle Swarm Technique for Treating Optimization Problems

Abstract

Swarm intelligence (SI) is considered one of the most popular computational intelligence paradigms. It originated from the study of colonies, or swarms of social organisms. Studies of the social behavior of organisms (individuals) in swarms prompted the design of very efficient optimization and clustering algorithms used to solve difficult optimization problems by simulating natural evolution over populations of candidate solutions. One of these algorithms is the particle swarm optimization algorithm that we will focus in this thesis. Particle swarm optimization (PSO) is a stochastic optimization approach, modeled on the social behavior of bird flocks. In PSO, individuals, referred to as particles, are "flown" through hyper dimensional search space. Changes to the position of particles within the search space are based on the social-psychological tendency of individuals to emulate the success of other individuals. The changes to a particle within the swarm are therefore influenced by the experience, or knowledge, of its neighbors. The search behavior of a particle is affected by that of other particles within the swarm, where each particle represents a candidate solution to the problem at hand. The PSO particle positions oscillate in damped sinusoidal waves until they converge to points in between their previous best positions and the global best positions discovered by all particles so far. If some point visited by a particle during this oscillation has better fitness than its previous best position, then the particle movement generally converges to the global best position discovered so far. Also the