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An Improved Reptile Search Algorithm Based on Lévy Flight and Interactive Crossover Strategy to Engineering Application

Author

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  • Liqiong Huang

    (College of Mathematics and Computer Application, Shangluo University, Shangluo 726000, China
    Department of Applied Mathematics, Xi’an University of Technology, Xi’an 710054, China)

  • Yuanyuan Wang

    (Electronic Information and Electrical Engineering College, Shangluo University, Shangluo 726000, China)

  • Yuxuan Guo

    (Department of Applied Mathematics, Xi’an University of Technology, Xi’an 710054, China)

  • Gang Hu

    (Department of Applied Mathematics, Xi’an University of Technology, Xi’an 710054, China)

Abstract

In this paper, we propose a reptile search algorithm based on Lévy flight and interactive crossover strategy (LICRSA), and the improved algorithm is employed to improve the problems of poor convergence accuracy and slow iteration speed of the reptile search algorithm. First, the proposed algorithm increases the variety and flexibility of the people by introducing the Lévy flight strategy to prevent premature convergence and improve the robustness of the population. Secondly, an iteration-based interactive crossover strategy is proposed, inspired by the crossover operator and the difference operator. This strategy is applied to the reptile search algorithm (RSA), and the convergence accuracy of the algorithm is significantly improved. Finally, the improved algorithm is extensively tested using 2 test sets: 23 benchmark test functions and 10 CEC2020 functions, and 5 complex mechanical engineering optimization problems. The numerical results show that LICRSA outperforms RSA in 15 (65%) and 10 (100%) of the 2 test sets, respectively. In addition, LICRSA performs best in 10 (43%) and 4 (40%) among all algorithms. Meanwhile, the enhanced algorithm shows superiority and stability in handling engineering optimization.

Suggested Citation

  • Liqiong Huang & Yuanyuan Wang & Yuxuan Guo & Gang Hu, 2022. "An Improved Reptile Search Algorithm Based on Lévy Flight and Interactive Crossover Strategy to Engineering Application," Mathematics, MDPI, vol. 10(13), pages 1-39, July.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:13:p:2329-:d:854821
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    References listed on IDEAS

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    1. 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.
    2. Chou, Jui-Sheng & Truong, Dinh-Nhat, 2021. "A novel metaheuristic optimizer inspired by behavior of jellyfish in ocean," Applied Mathematics and Computation, Elsevier, vol. 389(C).
    3. Hashim, Fatma A. & Houssein, Essam H. & Hussain, Kashif & Mabrouk, Mai S. & Al-Atabany, Walid, 2022. "Honey Badger Algorithm: New metaheuristic algorithm for solving optimization problems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 192(C), pages 84-110.
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    Cited by:

    1. Yongqiang Sun & Xianchun Wang & Lijuan Gao & Haiyue Yang & Kang Zhang & Bingxiang Ji & Huijuan Zhang, 2024. "Multi-Objective Optimal Scheduling for Microgrids—Improved Goose Algorithm," Energies, MDPI, vol. 17(24), pages 1-29, December.
    2. Khizer Mehmood & Naveed Ishtiaq Chaudhary & Zeshan Aslam Khan & Khalid Mehmood Cheema & Muhammad Asif Zahoor Raja & Ahmad H. Milyani & Abdullah Ahmed Azhari, 2022. "Dwarf Mongoose Optimization Metaheuristics for Autoregressive Exogenous Model Identification," Mathematics, MDPI, vol. 10(20), pages 1-21, October.

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