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Title: An adaptive mutation operator for particle swarm optimization
Authors: Li, C
Yang, S
Korejo, I
Issue Date: 2008
Publisher: MIC 2008
Citation: The 2008 UK Workshop on Computational Intelligence: 165 - 170
Abstract: Particle swarm optimization (PSO) is an effcient tool for optimization and search problems. However, it is easy to betrapped into local optima due to its in-formation sharing mechanism. Many research works have shown that mutation operators can help PSO prevent prema- ture convergence. In this paper, several mutation operators that are based on the global best particle are investigated and compared for PSO. An adaptive mutation operator is designed. Experimental results show that these mutation operators can greatly enhance the performance of PSO. The adaptive mutation operator shows great advantages over non-adaptive mutation operators on a set of benchmark test problems.
Description: Copyright @ 2008 MIC
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Computer Science
Dept of Computer Science Research Papers

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