Particle swarm optimization with stochastic local search

Abstract

In Particle Swarm Optimization (PSO) algorithm, the acting of gBest particle during evolutionary process is important for attaining convergence. A new improved PSO algorithm called SLS-PSO was proposed in this paper. Based on the structure of basic PSO algorithm, the proposed algorithm searched local optimal solutions to gBest particle by adopting Stochastic Local Search (SLS) algorithm to improve the convergence performance during evolutionary process of PSO algorithm. Four well-designed test problems were used to evaluate the proposed algorithm. Compared with the basic PSO algorithm, the proposed algorithm shows its effectiveness and efficiency.