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Bilevel-search particle swarm optimization for computationally expensive optimization problems

  • Optimization
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Abstract

Population-based global optimization algorithms often need few computational costs to find good regions which contain potential optima, while much more are needed to refine them for higher accuracies in the computationally expensive optimization problems. Such phenomenon is termed “asymptotic inefficiency” phenomenon in this paper. Inspired by the great success of bilevel or multilevel algorithms in eliminating similar phenomenon, we present the bilevel-search framework to alleviate the “asymptotic inefficiency” and apply it to SPSO2011. The main features of the proposed framework adopted to SPSO2011 are: (1) the order-2 stable in the theory and (2) the simple but efficient bilevel-search framework. The extensive numerical experiments show that the proposal framework successfully alleviates the “asymptotic inefficiency” of SPSO2011, and the proposal is a promising global optimization algorithm compared with two popular particle swarm optimization algorithms.

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Data availability

The data used to support the findings of this study are available from the corresponding author upon request.

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Funding

This work was supported in part by the National Science Fund of China under Grant 61773119, part of the Key Project of Science and Technology Innovation 2030 supported by the Ministry of Science and Technology of China under Grant 2018AAA0101300, and in part by the Guangdong Universities’ Special Projects in Key Fields of the Natural Science under Grant 2019KZDZX1005.

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Authors and Affiliations

  1. College of Computer Science and Technology, Dongguan University of Technology, Dongguan, 523808, China

    Yuan Yan, Qin Zhou & Qunfeng Liu

  2. School of Computer Science, Shaanxi Normal University, Xi’an, 710119, China

    Shi Cheng

  3. Shenzhen Institution for Advanced Study, University of Electronic Science and Technology of China, Shenzhen, 518110, China

    Yun Li

  4. i4AI Ltd, London, WCIN3AX, UK

    Yun Li

Authors
  1. Yuan Yan
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  2. Qin Zhou
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  3. Shi Cheng
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  4. Qunfeng Liu
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  5. Yun Li
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Contributions

YY was involved in writing—original, visualization, data curation, resources, formal analysis, methodology, and software. QZ contributed to software. SC was involved in writing—review and editing. QL was involved in conceptualization and writing—review and editing. YL was involved in supervision.

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Correspondence to Qunfeng Liu.

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This work was supported in part by the National Science Fund of China under Grant 61773119, part of the Key Project of Science and Technology Innovation 2030 supported by the Ministry of Science and Technology of China under Grant 2018AAA0101300, and in part by the Guangdong Universities’ Special Projects in Key Fields of the Natural Science under Grant 2019KZDZX1005.

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Yan, Y., Zhou, Q., Cheng, S. et al. Bilevel-search particle swarm optimization for computationally expensive optimization problems. Soft Comput 25, 14357–14374 (2021). https://doi.org/10.1007/s00500-021-06169-3

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