Journal of Jishou University(Natural Sciences Edition)

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Adaptive Quantum Particle Swarm Optimization Algorithm Based on Normal Cloud Model

YU Dawei, ZHOU Haipeng, SUN Min, LI Yang, ZHANG Enbao, LI Qianqian   

  1.  (1.School of Information & Computer Science, Anhui Agriculture University, Hefei 230036, China; 2.Key Laboratory of  Technology Integration and Application in Agricultural Internet of Things (Anhui Agriculture University),  Ministry of Agriculture, Hefei 230036, China; 3.State Grid Anhui Electric Power Co., Ltd.  Xuancheng Power Supply Company, Xuancheng 242000, Anhui China)
  • Online:2019-11-25 Published:2019-12-16


Based on the randomness, fuzziness and stability  of cloud model,  an adaptive quantum particle swarm optimization algorithm CMAQPSO based on normal cloud model is proposed. The algorithm uses X conditional cloud generator to control the contraction expansion coefficient of QPSO algorithm, and uses Y condition cloud generator to construct the mutation operator of QPSO algorithm. A quantum well center adjustment strategy and boundary correction strategy are proposed. The experimental results show that the average optimization effect of the CMAQPSO algorithm on the five test functions is significantly better than the other three algorithms (SPSO, OPSO,CVCPSO).

Key words: normal cloud model, self-adaptive, quantum particle swarm, quantum-behaved particle swarm optimization algorithm

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