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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Wei Fang & Jun Sun & Wenbo Xu, 2009. "Analysis Of Mutation Operators On Quantum-Behaved Particle Swarm Optimization Algorithm," New Mathematics and Natural Computation (NMNC), World Scientific Publishing Co. Pte. Ltd., vol. 5(02), pages 487-496.
    • Handle: RePEc:wsi:nmncxx:v:05:y:2009:i:02:n:s179300570900143x
    DOI: 10.1142/S179300570900143X
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