Dynamic Multi-swarm Particle Swarm Optimization Based on Elite Learning

Abstract

This paper presents a dynamic multi-swarm particle swarm optimization based on elite learning (DMS-PSO-EL) that consists of two kinds of sub-swarms to trade-off between exploitation and exploration capabilities. In DMS-PSO-EL, the whole population is divided into several DMS sub-swarms and one following sub-swarm on the basis of the fitness value rankings. In the evolution process, these DMS sub-swarms provide the exploration ability through dynamic regrouping strategy, while following sub-swarm enhances the exploitation ability by learning elite particles from DMS sub-swarms. Besides, randomly regrouping schedule regroups the entire population in each regrouping period aiming to avoid premature convergence and enhance inferior particles’ searching ability. Comparing DMSPSO-EL with other 8 peer algorithms on CEC2013 benchmark functions, the results suggest that DMS-PSO-EL demonstrates superior performance for solving different types of functions. Besides that, the massive experiments show the superiority of the proposed strategy used in DMS-PSO-EL.