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Quadratic interpolation boosted black widow spider-inspired optimization algorithm with wavelet mutation

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Listed:
  • Hu, Gang
  • Du, Bo
  • Li, Huinan
  • Wang, Xupeng

Abstract

Meta-heuristic algorithms are effective in solving complex optimization problems with advantages of flexibility for coding, robustness and global optimization capability. An enhanced Black Widow Optimization called QIWBWO algorithm with three improvement strategies is proposed in this paper. At the beginning of search, the theory of good points set is used to obtain the better initial population, which helps the algorithm to quickly determine the correct search direction. Then, quadratic interpolation strategy is used to improve the solution accuracy and accelerate the convergence. Meanwhile, to avoid the algorithm falling into a local optimum, wavelet mutation is introduced to improve population diversity and helps the algorithm to search the global optimum rather than local optimums. The proposed BWO algorithm is compared with other different kinds of meta-heuristic algorithms on 25 traditional benchmark functions and CEC2017 competition suite. The statistical results show the improved BWO algorithm delivers better performance in accuracy, stability and convergence rate. Finally, QIWBWO also obtains the best results on four classical optimization problems in engineering application, which verifies its practicality and effectiveness. The source code of QIWBWO is publicly available in the supplementary material related to this article.

Suggested Citation

  • Hu, Gang & Du, Bo & Li, Huinan & Wang, Xupeng, 2022. "Quadratic interpolation boosted black widow spider-inspired optimization algorithm with wavelet mutation," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 200(C), pages 428-467.
    • Handle: RePEc:eee:matcom:v:200:y:2022:i:c:p:428-467
    DOI: 10.1016/j.matcom.2022.04.031
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    References listed on IDEAS

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    1. Luo, Qifang & Yang, Xiao & Zhou, Yongquan, 2019. "Nature-inspired approach: An enhanced moth swarm algorithm for global optimization," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 159(C), pages 57-92.
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    3. Hu, Gang & Dou, Wanting & Wang, Xiaofeng & Abbas, Muhammad, 2022. "An enhanced chimp optimization algorithm for optimal degree reduction of Said–Ball curves," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 197(C), pages 207-252.
    4. Yan, Zheping & Zhang, Jinzhong & Zeng, Jia & Tang, Jialing, 2021. "Nature-inspired approach: An enhanced whale optimization algorithm for global optimization," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 185(C), pages 17-46.
    5. Xiao-dong Guo & Xue-liang Zhang & Li-fang Wang, 2020. "Fruit Fly Optimization Algorithm Based on Single-Gene Mutation for High-Dimensional Unconstrained Optimization Problems," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-8, November.
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    Cited by:

    1. Jianwei Yang & Zhen Liu & Xin Zhang & Gang Hu, 2022. "Elite Chaotic Manta Ray Algorithm Integrated with Chaotic Initialization and Opposition-Based Learning," Mathematics, MDPI, vol. 10(16), pages 1-34, August.
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    3. Turgut, Oguz Emrah & Turgut, Mert Sinan, 2023. "Local search enhanced Aquila optimization algorithm ameliorated with an ensemble of Wavelet mutation strategies for complex optimization problems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 206(C), pages 302-374.
    4. Hu, Gang & Yang, Rui & Wei, Guo, 2023. "Hybrid chameleon swarm algorithm with multi-strategy: A case study of degree reduction for disk Wang–Ball curves," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 206(C), pages 709-769.

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