Particle Swarm Optimization with Exploratory Move

Abstract

Particle Swarm Optimization (PSO) algorithm is a swarm based algorithm deliver good performance in many optimization problems. However, PSO has tendency of trapping into local optima. In the paper, an improved PSO algorithm has been proposed by employing Exploratory Move on global best particle of the swarm called as PSO with exploratory move (ExPSO) algorithm. In the proposed approach in order to preventing PSO algorithm from trapping into local optima, particles are jumped to an unknown position made by the exploratory move. The performance of the ExPSO algorithm has been investigated on a set of eight standard benchmark functions and results are compared with the simple PSO, constriction factor PSO (CFPSO) and inertia weight PSO (IWPSO). The numerical results show that the ExPSO algorithm performs better, robust and statistically significant on most of the test cases.