%A Tianfei Chen, Lijun Sun, Xiaodong Song, and Haixu Niu %T An Adaptive Cooperative Dual Particle Swarm Optimization Algorithm with Chaotic Mutation and Quantum Behavior %0 Journal Article %D 2019 %J Int J Performability Eng %R 10.23940/ijpe.19.10.p9.26362644 %P 2636-2644 %V 15 %N 10 %U {https://www.ijpe-online.com/CN/abstract/article_4253.shtml} %8 2019-10-20 %X

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.