Int J Performability Eng ›› 2019, Vol. 15 ›› Issue (10): 2636-2644.doi: 10.23940/ijpe.19.10.p9.26362644

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An Adaptive Cooperative Dual Particle Swarm Optimization Algorithm with Chaotic Mutation and Quantum Behavior

Tianfei Chenab, Lijun Sunab*, Xiaodong Songab, and Haixu Niuc   

  1. aSchool of Electrial Engineering, Henan University of Technology, Zhengzhou, 450001, China
    bZhengzhou Key Laboratory of Machine Perception and Intelligence Systems, Zhengzhou, 450001, China
    cBelarusian State Pedagogical University, Minsk, 220030, Belarus
  • Submitted on ; Revised on ; Accepted on
  • Contact: Sun Lijun
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    * Corresponding author.E-mail address:

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    Fund The authors sincerely thank the editors and reviewers for their valuable suggestions and useful comments that have led to the present improved version of the original manuscript This work was supported in part by the National Natural Science Foundation of China (No U1604151, 61803146), Outstanding Talent Project of Science and Technology Innovation in Henan Province (No 174200510008), Science and Technology Project of Henan Province (No 182102210094), Natural Science Project of Education Department of Henan Province (No 18A510001), and Fundamental Research Funds of Henan University of Technology (No 2015QNJH13, 2016XTCX06)


An adaptive cooperative dual particle swarm optimization algorithm with chaotic mutation and quantum behavior is proposed to solve the contradiction between global search and local refinement search for basic particle swarm optimization algorithms. The strategy of adaptive cooperative evolution for two subgroups is used to parallel search, the subgroup with the chaotic mutation operator modifies the historical optimal position of particles and the subgroup optimal position using the principle of chaotic randomly ergodicity, and the chaotic mutation radius is increasing with the iterative evolution to enhance the global search ability. Additionally, in order to improve the local refinement search ability, the subgroup with quantum behavior, which casts off the searching orbital, updates the average optimal position of the subgroup and the subgroup optimal position during evolution. Finally, the numerical simulation results demonstrate that the proposed algorithm not only has fast convergence speed and high convergence accuracy, but also has significant advantages in dimension expansion.

Key words: particle swarm optimization, chaotic mutation, quantum behavior, adaptive cooperative