Gaussian Particle Swarm with Jumps

Abstract

Gaussian particle swarm optimization (GPSO) algorithm has shown promising results for solving multimodal optimization problems in low dimensional search space. But similar to evolutionary algorithms (EAs), GPSO may also get stuck in local minima when optimizing functions with many local minima like the Rastrigin or Riewank functions in high dimensional search space. In this paper, an approach which consists of a GPSO with jumps to escape from local minima is presented. The jump strategy is implemented as a mutation operator based on the Gaussian and Cauchy probability distribution. The new algorithm was tested on a suite of well-known benchmark functions with many local optima and the results were compared with those obtained by the standard PSO algorithm, and PSO with constriction factor. Simulation results show that the GPSO with Gaussian and Cauchy jump outperforms the standard one and presents a very competitive performance compared to PSO with constriction factor and also self-adaptive evolutionary programming.