A Center Multi-swarm Cooperative Particle Swarm Optimization with Ratio and Proportion Learning

Abstract

This paper presents a center multi-swarm cooperative PSO with ratio and proportion learning (CMCPSO-RP), employing two well-known psychology theories. In the original MCPSO-CC, the convergence speed can be accelerated which comes at decreasing the diversity of sub-swarms, suffering from premature convergence. There is no mechanism to guarantee every possible region of the search space could be searched. To tackle this problem, all best particles from each sub-swarm can be collected and sent to master swarm to maintain a population of potential solutions. This process is less prone to becoming trapped in local minima, but typically has lower efficiency of iterations. To balance the ability of exploration and exploitation, a ratio and proportion learning strategy is proposed by empowering the searching particles with human-like thinking and cognitive process, inspired by Cognitive Load Theory and Human Problem Solving Theory. In our approach, a reasonable ratio design can be not only a way to exhibit a solution quality versus speed tradeoff, but also make CMCPSO-RP more in line with the laws of regular learning in nature. Application of the newly developed PSO algorithm on several benchmark optimization problems shows a marked improvement in performance over the comparison algorithms on all test functions.