Multiple choice strategy for PSO algorithm enhanced with dimensional mutation

Abstract

In this study the promising Multiple-choice strategy for PSO (MC-PSO) is enhanced with the blind search based single dimensional mutation. The MC-PSO utilizes principles of heterogeneous swarms with random behavior selection. The performance previously tested on both large-scale and fast optimization is significantly improved by this approach. The newly proposed algorithm is more robust and resilient to premature convergence than both original PSO and MC-PSO. The performance is tested on four typical benchmark functions with variety of dimension settings.