A Particle Swarm with Selective Particle Regeneration for Multimodal Functions
نویسندگان
چکیده
This paper proposes an improved particle swarm optimization (PSO). In order to increase the efficiency, suggestions on parameter settings is made and a mechanism is designed to prevent particles fall into the local optimal. To evaluate its effectiveness and efficiency, this approach is applied to multimodal function optimizing tasks. 16 benchmark functions were tested, and results were compared with those of PSO, HNMPSO and GA-PSO. It shows the proposed method is both robust and suitable for multimodal function optimization. Key-Words: Particle Swarm Optimization, Cognitive and Social Parameter, Selective Particle Regeneration, Mutation Operation, Multimodal functions,
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