Multi-objective particle swarm optimization algorithm based on game strategies

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Multi-objective particle swarm optimization algorithm based on game strategies

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ACM/SIGEVO Summit on Genetic and Evolutionary Computation archive
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation table of contents

Shanghai, China

SESSION: Full papers table of contents

Pages 287-294

Year of Publication: 2009

ISBN:978-1-60558-326-6

Authors

Zhiyong Li	School of Computer and Communication, Hunan University, Changsha, China
Songbing Liu	School of Computer and Communication, Hunan University, Changsha, China
Degui Xiao	School of Computer and Communication, Hunan University, Changsha, China
Jun Chen	Student Admission Of Hunan University, Changsha, China
Kenli Li	School of Computer and Communication, Hunan University, Changsha, China

Sponsors

SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation
ACM: Association for Computing Machinery

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ACM New York, NY, USA

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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.

REFERENCES

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