Particle Swarm Optimization (PSO) is an evolutionary computation technique developed by Kennedy and Eberhart.It exhibits common evolutionary computation attributes including initialization with a population of random solutions and searching for optima by updating generations.Particle swarm optimization (PSO) is a population-based stochastic optimization algorithm motivated by intelligent collective behavior of some animals such as flocks of birds or schools of fish.Particles in a swarm are related socially; that is, each particle is a member of one or more neighborhoods. Each individual tries to emulate the behavior of the best of its neighbors. Each individual can be thought of as moving through the feature space with a velocity vector that is influenced by its neighbors.The purpose of optimization is to achieve the “best” design relative to a set of prioritized criteria or constraints. These include maximizing factors such as productivity, strength, reliability, longevity, efficiency, and utilization.