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Hybrid chameleon swarm algorithm with multi-strategy: A case study of degree reduction for disk Wang–Ball curves

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Listed:
  • Hu, Gang
  • Yang, Rui
  • Wei, Guo

Abstract

In this paper, an enhanced hybrid chameleon swarm algorithm (CSA) is proposed and applied to the degree reduction problem of disk Wang–Ball (DWB) curve. CSA is a novel population-based algorithm inspired by the hunting behavior of chameleons, its simplicity and easy implementation make it applied to different fields. However, it suffers from premature convergence and easy to fall into local optimum, especially in the face of complex optimization problems. Therefore, this paper proposes an enhanced hybrid CSA (CCECSA, for short). Compared with the classic CSA, the proposed CCECSA mainly introduces three improvements: (1) The crisscross optimization algorithm is mixed to avoid premature convergence, in which the horizontal and vertical crossover can generate moderation solutions to increase the diversity of the population. (2) Elite guidance mechanism is introduced to speed up the convergence. (3) Competitive substitution mechanism is added to replace the worst individual, and an interference strategy is set to prevent the algorithm from falling into a local optimum. The efficiency and robustness of the proposed CCECSA are demonstrated by the comparison results with some advanced meta-heuristic algorithms on CEC2014, CEC2017, and 4 engineering design examples. In addition, for the degree reduction problem of DWB curves, the multi-degree reduction optimization models of its center curve and radius function are established respectively. At the same time, the optimal center curve and radius function of the approximating DWB curves of lower degree are obtained by the proposed CCECSA. The experimental results show that the proposed CCECSA achieves the optimal solution with better convergence and robustness. The source code of CCECSA is publicly available in the supplementary material related to this article.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:matcom:v:206:y:2023:i:c:p:709-769
    DOI: 10.1016/j.matcom.2022.12.001
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    References listed on IDEAS

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    1. Rizk-Allah, Rizk M. & Hassanien, Aboul Ella & Snášel, Václav, 2022. "A hybrid chameleon swarm algorithm with superiority of feasible solutions for optimal combined heat and power economic dispatch problem," Energy, Elsevier, vol. 254(PC).
    2. Mokhtar Said & Ali M. El-Rifaie & Mohamed A. Tolba & Essam H. Houssein & Sanchari Deb, 2021. "An Efficient Chameleon Swarm Algorithm for Economic Load Dispatch Problem," Mathematics, MDPI, vol. 9(21), pages 1-14, November.
    3. Meng, Anbo & Zeng, Cong & Wang, Peng & Chen, De & Zhou, Tianmin & Zheng, Xiaoying & Yin, Hao, 2021. "A high-performance crisscross search based grey wolf optimizer for solving optimal power flow problem," Energy, Elsevier, vol. 225(C).
    4. 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.
    5. 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.
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