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Research on crow swarm intelligent search optimization algorithm based on surrogate model

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Abstract

A large amount of calculation exists in a complex engineering optimization problem. The swarm intelligence algorithm can improve calculation efficiency and accuracy of complex engineering optimization. In the existing research, the surrogate model and the swarm intelligence algorithm are only two independent tools to solve the optimization problem. In this paper, we propose the surrogate-assisted crow swarm intelligent search optimization algorithm (SACSA) by combining the characteristics of swarm intelligence algorithm and surrogate model. The proposed algorithm utilizes the initial samples to construct the surrogate model, and then the improved crow search algorithm (CSA) is applied to obtain optimal solution. Finally, the proposed algorithm is compared with EGO, MSSR, ARSM-ISES, AMGO and SEUMRE, MPS, HAM algorithms. The comparison results show that the proposed algorithm can find a global optimal solution with fewer samples and is beneficial to improving the efficiency and accuracy of calculation.

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Acknowledgments

This research is supported by the National Natural Science Foundation of China (Grant No. 51475082).

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Authors and Affiliations

  1. University of Electronic Science and Technology of China, Chengdu, 611731, China

    Huanwei Xu, Liangwen Liu & Miao Zhang

Authors
  1. Huanwei Xu
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  2. Liangwen Liu
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  3. Miao Zhang
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Corresponding author

Correspondence to Huanwei Xu.

Additional information

Huanwei Xu is an Associate Professor in Electronic Science and Technology of China. His research interests include multidiscipline design optimization, intellient optimization algorithm and surrogate model and time-varying reliability.

Liangwen Liu is a graduate student in Mechanical and Electrical Engineering, University of Electronic Science and Technology of China. His main research direction is surrogate model based optimization and intelligent algorithm.

Miao Zhang is a graduate student in Mechanical and Electrical Engineering, University of Electronic Science and Technology of China. Her main research direction is reliability optimization design.

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Xu, H., Liu, L. & Zhang, M. Research on crow swarm intelligent search optimization algorithm based on surrogate model. J Mech Sci Technol 34, 4043–4049 (2020). https://doi.org/10.1007/s12206-020-2215-8

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  • DOI: https://doi.org/10.1007/s12206-020-2215-8

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