Gaussian particle swarm optimization with differential evolution mutation

Abstract

During the past decade, the particle swarm optimization (PSO) with various versions showed competitiveness on the constrained optimization problems. In this paper, an improved Gaussian particle swarm optimization algorithm (GPSO) is proposed to improve the diversity and local search ability of the population. A mutation operator based on differential evolution (DE) is designed and employed to update the personal best position of the particle and the global best position of the population. The purpose is to improve the local search ability of GPSO and the probability to find the global optima. The regeneration strategy is employed to update the stagnated particle so as further to improve the diversity of GPSO. A simple feasibility-based method is employed to compare the performances of different particles. Simulation results of three constrained engineering optimization problems demonstrate the effectiveness of the proposed algorithm. © 2011 Springer-Verlag.