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Improved Harris hawks optimization for non-convex function optimization and design optimization problems

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  • Kang, Helei
  • Liu, Renyun
  • Yao, Yifei
  • Yu, Fanhua

Abstract

Harris hawks optimization (HHO) is a nature-inspired algorithm. It has the advantages of very few parameters, a simple structure, fast convergence and strong local search capability. The main drawback of the Harris hawks optimization is that it can easily fall into a local optimum. To solve this problem, a novel mutant strategy based on Brownian motion is proposed to combine with the original HHO. This mutant strategy is driven by exploiting the randomness of Brownian motion and does not require location information between populations and user-set parameters. As a result, it can guide the algorithm to better jump out of the local optimum trap. To verify the performance of the proposed algorithm, numerical experiments are carried out to compare the proposed algorithm with heuristic optimization algorithms for 54 non-convex functions and two classic engineering design problems. The results show that our algorithm not only escapes the local optimum trap, but also has better robustness and convergence.

Suggested Citation

  • Kang, Helei & Liu, Renyun & Yao, Yifei & Yu, Fanhua, 2023. "Improved Harris hawks optimization for non-convex function optimization and design optimization problems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 204(C), pages 619-639.
    • Handle: RePEc:eee:matcom:v:204:y:2023:i:c:p:619-639
    DOI: 10.1016/j.matcom.2022.09.010
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. Ren, Hao & Li, Jun & Chen, Huiling & Li, ChenYang, 2021. "Adaptive levy-assisted salp swarm algorithm: Analysis and optimization case studies," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 181(C), pages 380-409.
    4. Kumar, Yashveer & Singh, Vineet Kumar, 2021. "Computational approach based on wavelets for financial mathematical model governed by distributed order fractional differential equation," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 190(C), pages 531-569.
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    Cited by:

    1. Lei Chen & Yikai Zhao & Yunpeng Ma & Bingjie Zhao & Changzhou Feng, 2023. "Improving Wild Horse Optimizer: Integrating Multistrategy for Robust Performance across Multiple Engineering Problems and Evaluation Benchmarks," Mathematics, MDPI, vol. 11(18), pages 1-35, September.
    2. Khan, Taimoor Ali & Chaudhary, Naveed Ishtiaq & Khan, Zeshan Aslam & Mehmood, Khizer & Hsu, Chung-Chian & Raja, Muhammad Asif Zahoor, 2024. "Design of Runge-Kutta optimization for fractional input nonlinear autoregressive exogenous system identification with key-term separation," Chaos, Solitons & Fractals, Elsevier, vol. 182(C).

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