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A grouping particle swarm optimizer

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Abstract

Due to the lack of global search capacity, most evolutionary or swarm intelligence based algorithms show their inefficiency when optimizing multi-modal problems. In this paper, we propose a grouping particle swarm optimizer (GPSO) to solve this kind of problem. In the proposed algorithm, the swarm consists of several groups. For every several iterations, an elite group is constructed and used to replace the worst one. The thought of grouping is helpful for improving the diversity of the solutions, and then enhancing the global search ability of the algorithm. In addition, we apply a simple mutation operator to the best solution so as to help it escape from local optima. The GPSO is compared with several variants of particle swarm optimizer (PSO) and some state-of-the-art evolutionary algorithms on CEC15 benchmark functions and three practical engineering problems. As demonstrated by the experimental results, the proposed GPSO outperforms its competitors in most cases.

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Authors and Affiliations

  1. Engineering Research Institute, Guangzhou College of South China University of Technology, Guangzhou, 510800, People’s Republic of China

    Xiaorong Zhao & Yuren Zhou

  2. School of Data and Computer Science, Collaborative Innovation Center of High Performance Computing, Sun Yat-sen University, Guangzhou, 510006, People’s Republic of China

    Xiaorong Zhao & Yuren Zhou

  3. School of Software Engineering, South China University of Technology, Guangzhou, 510006, People’s Republic of China

    Yi Xiang

Authors
  1. Xiaorong Zhao
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  2. Yuren Zhou
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  3. Yi Xiang
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Correspondence to Yuren Zhou.

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Zhao, X., Zhou, Y. & Xiang, Y. A grouping particle swarm optimizer. Appl Intell 49, 2862–2873 (2019). https://doi.org/10.1007/s10489-019-01409-4

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