An Overview of Particle Swarm Optimization Variants

Abstract

Particle swarm optimization (PSO) is a stochastic algorithm used for the optimization problems proposed by Kennedy [1] in 1995. It is a very good technique for the optimization problems. But still there is a drawback in the PSO is that it stuck in the local minima. To improve the performance of PSO, the researchers proposed the different variants of PSO. Some researchers try to improve it by improving initialization of the swarm. Some of them introduce the new parameters like constriction coefficient and inertia weight. Some researchers define the different method of inertia weight to improve the performance of PSO. Some researchers work on the global and local best particles by introducing the mutation operators in the PSO. In this paper, we will see the different variants of PSO with respect to initialization, inertia weight and mutation operators.