Particles Swarm Optimization Techniques , Principle, Comparison , Application

Abstract

Particle swarm optimization (PSO) is a computational method that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality. It solves a problem by having a population of candidate solutions, here dubbed particles, and moving these particles around in the search-space according to simple mathematical formulae over the particle's position and velocity. Each particle's movement is influenced by its local best -known position (pbest), but is also guided toward the best -known positions (gbest) in the search-space, which are updated as better positions are found by other particles. This is expected to move the swarm toward the best solutions. The particles move in the search space with considering its own velocity and position called as pbest, but pbest has the tendency to flow around the local optima. Because of this problem we compare the different Particle swarm optimization based algorithm with its principles & application in this paper. Variable Neighbourhood PSO, Adaptive PSO & Niche PSO compare to see the performance of the particles in the search space with respect to time.