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<p>The Golden Jackal Optimization algorithm (GJO) is a Swarm Intelligence (SI) algorithm utilized for tackling arduous problems in undefined search spaces. In order to address the issues of slow convergence and difficulty in evading local optima encountered by GJO, the Multi-Strategy Golden Jackal Optimization (MSGJO) algorithm is proposed. Three strategies were implemented to enhance the original GJO. Firstly, the utilization of a Good Point Set, as opposed to random numbers, improves the quality of the initial population. Secondly, the introduction of a Quasi-Opposition-Based Learning strategy facilitates the balancing of algorithmic exploration and exploitation capabilities, leading to enhanced convergence efficiency. Lastly, the maintenance of population diversity during the iterative process is achieved through the adoption of the Cauchy mutation and chaotic circle perturbation strategies. A comparative analysis was conducted on 23 benchmark functions using fourteen SI algorithms. Additionally, MSGJO was applied to the CEC2019 competition suite, successfully solving two distinct engineering design problems. The results demonstrate that MSGJO exhibits significant superiority not only in solving problems involving global optimal solutions but also in addressing challenging constrained problems. The code for this study will be available at <a href="https://github.com/ProfYangPaperCode/MSGJO-code">https://github.com/ProfYangPaperCode/MSGJO-code</a>.</p>

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Multi-Strategy Golden Jackal Optimization for engineering design

  • 期刊论文

  • 发表日期：

    2025年3月13日

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  • Wenbiao Yang,
  • Tingfeng Lai,
  • Yuhui Fang

摘要

The Golden Jackal Optimization algorithm (GJO) is a Swarm Intelligence (SI) algorithm utilized for tackling arduous problems in undefined search spaces. In order to address the issues of slow convergence and difficulty in evading local optima encountered by GJO, the Multi-Strategy Golden Jackal Optimization (MSGJO) algorithm is proposed. Three strategies were implemented to enhance the original GJO. Firstly, the utilization of a Good Point Set, as opposed to random numbers, improves the quality of the initial population. Secondly, the introduction of a Quasi-Opposition-Based Learning strategy facilitates the balancing of algorithmic exploration and exploitation capabilities, leading to enhanced convergence efficiency. Lastly, the maintenance of population diversity during the iterative process is achieved through the adoption of the Cauchy mutation and chaotic circle perturbation strategies. A comparative analysis was conducted on 23 benchmark functions using fourteen SI algorithms. Additionally, MSGJO was applied to the CEC2019 competition suite, successfully solving two distinct engineering design problems. The results demonstrate that MSGJO exhibits significant superiority not only in solving problems involving global optimal solutions but also in addressing challenging constrained problems. The code for this study will be available at https://github.com/ProfYangPaperCode/MSGJO-code.