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An Adaptive Dimension Weighting Spherical Evolution to Solve Continuous Optimization Problems

Author

Listed:
  • Yifei Yang

    (Faculty of Engineering, University of Toyama, Toyama-shi 930-8555, Japan)

  • Sichen Tao

    (Faculty of Engineering, University of Toyama, Toyama-shi 930-8555, Japan)

  • Shibo Dong

    (Faculty of Engineering, University of Toyama, Toyama-shi 930-8555, Japan)

  • Masahiro Nomura

    (Faculty of Engineering, University of Toyama, Toyama-shi 930-8555, Japan)

  • Zheng Tang

    (Faculty of Engineering, University of Toyama, Toyama-shi 930-8555, Japan)

Abstract

The spherical evolution algorithm (SE) is a unique algorithm proposed in recent years and widely applied to new energy optimization problems with notable achievements. However, the existing improvements based on SE are deemed insufficient due to the challenges arising from the multiple choices of operators and the utilization of a spherical search method. In this paper, we introduce an enhancement method that incorporates weights in individuals’ dimensions that are affected by individual fitness during the iteration process, aiming to improve SE by adaptively balancing the tradeoff between exploitation and exploration during convergence. This is achieved by reducing the randomness of dimension selection and enhancing the retention of historical information in the iterative process of the algorithm. This new SE improvement algorithm is named DWSE. To evaluate the effectiveness of DWSE, in this study, we apply it to the CEC2017 standard test set, the CEC2013 large-scale global optimization test set, and 22 real-world problems from CEC2011. The experimental results substantiate the effectiveness of DWSE in achieving improvement.

Suggested Citation

  • Yifei Yang & Sichen Tao & Shibo Dong & Masahiro Nomura & Zheng Tang, 2023. "An Adaptive Dimension Weighting Spherical Evolution to Solve Continuous Optimization Problems," Mathematics, MDPI, vol. 11(17), pages 1-17, August.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:17:p:3733-:d:1229211
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    References listed on IDEAS

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    1. Zhou, Hong & Cheung, Waiman & Leung, Lawrence C., 2009. "Minimizing weighted tardiness of job-shop scheduling using a hybrid genetic algorithm," European Journal of Operational Research, Elsevier, vol. 194(3), pages 637-649, May.
    2. Jian Zhao & Bochen Zhang & Xiwang Guo & Liang Qi & Zhiwu Li, 2022. "Self-Adapting Spherical Search Algorithm with Differential Evolution for Global Optimization," Mathematics, MDPI, vol. 10(23), pages 1-31, November.
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