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Analysis Of Mutation Operators On Quantum-Behaved Particle Swarm Optimization Algorithm

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Author Info

  • 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)

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    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.

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    Bibliographic Info

    Article provided by World Scientific Publishing Co. Pte. Ltd. in its journal New Mathematics and Natural Computation.

    Volume (Year): 05 (2009)
    Issue (Month): 02 ()
    Pages: 487-496

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    Handle: RePEc:wsi:nmncxx:v:05:y:2009:i:02:p:487-496

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    Related research

    Keywords: Particle swarm optimization; mutation operator; global convergence;

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