ANALYSIS OF MUTATION OPERATORS ON QUANTUM-BEHAVED PARTICLE SWARM OPTIMIZATION ALGORITHM

Wei Fang (), Jun Sun and Wenbo Xu
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Wei Fang: Center of Intelligent and High Performance Computing, School of Information Technology, Jiangnan University, Lihu Dadao 1800, Wuxi, 214122, P.R. China
Jun Sun: Center of Intelligent and High Performance Computing, School of Information Technology, Jiangnan University, Lihu Dadao 1800, Wuxi, 214122, P.R. China
Wenbo Xu: Center of Intelligent and High Performance Computing, School of Information Technology, Jiangnan University, Lihu Dadao 1800, Wuxi, 214122, P.R. China

New Mathematics and Natural Computation (NMNC), 2009, vol. 05, issue 02, 487-496

Abstract: Mutation operator is one of the mechanisms of evolutionary algorithms (EAs) and it can provide diversity in the search and help to explore the undiscovered search place. Quantum-behaved particle swarm optimization (QPSO), which is inspired by fundamental theory of PSO algorithm and quantum mechanics, is a novel stochastic searching technique and it may encounter local minima problem when solving multi-modal problems just as that in PSO. A novel mutation mechanism is proposed in this paper to enhance the global search ability of QPSO and a set of different mutation operators is introduced and implemented on the QPSO. Experiments are conducted on several well-known benchmark functions. Experimental results show that QPSO with some of the mutation operators is proven to be statistically significant better than the original QPSO.

Keywords: Particle swarm optimization; mutation operator; global convergence (search for similar items in EconPapers)
Date: 2009
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DOI: 10.1142/S179300570900143X

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