Dynamic Multi-Swarm Particle Swarm Optimization with Cooperative Coevolution for Large Scale Global Optimization
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National Natural Science Foundation of China (61673404, 61473266, 61876169); China Postdoctoral Science Foundation (2017M622373)

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    Abstract:

    With the development of engineering technology and the improvement of mathematical model, a large number of optimization problems have been developed from low dimensional optimization to large-scale complex optimization. Large scale global optimization is an active research topic in the real-parameter optimization. Based on the analysis of the characteristics of large scale problems, a stochastic dynamic cooperative coevolution strategy is proposed in the article. Additionally, a strategy is added to the dynamic multi-swarm particle swarm optimization algorithm. Then, the dual grouping of population and decision variables is realized. Next, the performance of the novel optimization on the set of benchmark functions provided for the CEC2013 special session on large scale optimization is reported. Finally the validity of the algorithm is verified by comparing with other algorithms.

    Reference
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    附中文参考文献:
    [2] 刘金鹏.面向大规模实值优化问题的CMA-ES算法及其分制策略研究[博士学位论文].合肥:中国科学技术大学,2014.
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梁静,刘睿,于坤杰,瞿博阳.求解大规模问题协同进化动态粒子群优化算法.软件学报,2018,29(9):2595-2605

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History
  • Received:May 01,2017
  • Revised:July 10,2017
  • Adopted:September 26,2017
  • Online: November 13,2017
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