Multi-objective particle swarm optimization algorithm based on game strategies

Authors:
Zhiyong Li;Songbing Liu;Degui Xiao;Jun Chen;Kenli Li
Affiliations:
School of Computer and Communication, Hunan University, Changsha, China;School of Computer and Communication, Hunan University, Changsha, China;School of Computer and Communication, Hunan University, Changsha, China;Student Admission Of Hunan University, Changsha, China;School of Computer and Communication, Hunan University, Changsha, China
Venue:
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
Year:
2009

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Abstract

Particle Swarm Optimization (PSO) is easier to realize and has a better performance than evolutionary algorithm in many fields. This paper proposes a novel multi-objective particle swarm optimization algorithm inspired from Game Strategies (GMOPSO), where those optimized objectives are looked as some independent agents which tend to optimize own objective function. Therefore, a multi- player game model is adopted into the multi-objective particle swarm algorithm, where appropriate game strategies could bring better multi-objective optimization performance. In the algorithm, novel bargain strategy among multiple agents and nondominated solutions archive method are designed for improving optimization performance. Moreover, the algorithm is validated by several simulation experiments and its performance is tested by different benchmark functions.