Cooperative particle swarm optimizer with elimination mechanism for global optimization of multimodal problems

Abstract

This paper presents a new particle swarm optimizer (PSO) that called the cooperative particle swarm optimizer with elimination mechanism (CPSO-EM) in an attempt to address the issue of getting trapped into local optimum when solving nonseparable multimodal problems using PSO algorithm. The proposed CPSO-EM builds on the basis of an early cooperative PSO (CPSO-H) that employs cooperative behavior. The CPSO- H and elimination mechanism (EM) memory are incorporated together to obtain CPSO-EM. Experimental studies on a set of test functions show that CPSO-EM exhibits better performance in solving nonseparable multimodal problems than several other peer algorithms. very low since the other parameters contained in the same subcomponent limit the performance. In this paper, we present a novel algorithm called cooperative particle swarm optimizer with elimination mechanism (CPSO-EM) to cope with the nonseparable problems. Based on an early cooperative PSO (CPSO-H) (6), CPSO-EM overcomes the problem of falling into local optimum by using elimination mechanism (EM) memory that increases the diversity of useful particles. The rest of this paper is organized as follows. In Section II, basic PSO algorithm and some variants are presented. Section III describes the CPSO-EM algorithm. Section IV describes the benchmark test functions and compares the performance of CPSO-EM with that of some peer algorithms taken from literature. Final conclusions are given in Section V.