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Boost particle swarm optimization with fitness estimation

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Abstract

It is well known that the classical particle swarm optimization (PSO) is time-consuming when used to solve complex fitness optimization problems. In this study, we perform in-depth research on fitness estimation based on the distance between particles and affinity propagation clustering. In addition, support vector regression is employed as a surrogate model for estimating fitness values instead of using the objective function. The particle swarm optimization algorithm based on affinity propagation clustering, the efficient particle swarm optimization algorithm, and the particle swarm optimization algorithm based on support vector regression machine are then proposed. The experimental results show that the new algorithms significantly reduce the computational counts of the objective function. Compared with the classical PSO, the optimization results exhibit no loss of accuracy or stability.

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Acknowledgements

The work was supported by National Natural Science Foundation of China under Grants 61503150, 61472158, 61572228 and Jilin Scientific and Technological Development Program under Grant 20160520012JH.

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Authors and Affiliations

  1. Key Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry, College of Computer Science and Technology, Jilin University, Changchun, 130012, China

    Lu Li, Yanchun Liang, Tingting Li, Chunguo Wu & Xiaosong Han

  2. Zhuhai Key Laboratory for Symbol Computation and Knowledge Engineering of Ministry of Education, Zhuhai College of Jilin University, Zhuhai, 519041, China

    Yanchun Liang

  3. Daqing Oilfield Exploration and Development Research Institute, Daqing Oilfield Personnel Development Institute, CNPC, Daqing, 163000, China

    Guozhong Zhao & Xiaosong Han

Authors
  1. Lu Li
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  2. Yanchun Liang
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  3. Tingting Li
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  4. Chunguo Wu
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  5. Guozhong Zhao
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  6. Xiaosong Han
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Correspondence to Xiaosong Han.

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Li, L., Liang, Y., Li, T. et al. Boost particle swarm optimization with fitness estimation. Nat Comput 18, 229–247 (2019). https://doi.org/10.1007/s11047-018-9699-5

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