A Ranking Weight Based Roulette Wheel Selection Method for Comprehensive Learning Particle Swarm optimization
Yuan-Peng Zhu, Qiang Yang, Xudong Gao, Zhenyu Lu · 2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC) · 2022
This paper proposes a ranking weight based roulette wheel selection (RWRWS) method for a promising particle swarm optimizer, called comprehensive learning particle swarm optimizer (CLPSO), to further improve its optimization performance. Specifically, the proposed RWRWS adopts a non-linear weight function to enhance the selection probabilities of promising personal best positions during the exemplar construction. In this way, it is expected that the construction efficiency of generating a promising leading exemplar for each particle could be improved and thus the optimization performance of CLPSO is expectedly elevated. To validate the feasibility and effectiveness of RWRWS, we carry out extensive experiments on a widely acknowledged benchmark problem set by comparing it with other three selection methods, namely the fitness-based roulette wheel selection (FRWS), the ranking based roulette wheel selection (RRWS), and the tournament selection (TS). Experimental results demonstrate that RWRWS helps CLPSO attain the best overall performance among the four selection methods.