PARTICLE SWARM OPTIMIZATION

Abstract

An optimisation algorithm based on the behaviors of social organisms is known as particle swarm optimizatio (PSO).It represents a set of potential answers to an optimi sation issue as a swarm of moving particles in the parameter space. The performance of the particles is guided by their own performance and the performance of their neighbors, leading to an optimized solution. This thesis presents a study of the impact of boundary conditions on the performance of Particle Swarm Optimization (PSO) through the use of the invisible wall technique. The convergence behaviors of PSO are analyzed and its application to discrete-valued problems and multi-objective optimization problems are discussed. Additionally, practical applications of PSO are explored. We are solved linear programming problems, transportation problem using Particle Swarm Optimization and applying on a Data Set.