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<p>To overcome the limitations of the Golden Jackal Optimization (GJO) algorithm in complex high-dimensional landscapes, such as insufficient population diversity, poor balance between exploration and exploitation, and a tendency to fall into local optima, a cooperative elite-guided double-encircling golden jackal optimization (CEGJO) algorithm is proposed in this study. First, the original single-guidance mechanism is removed, and multiple elite groups are introduced to cooperatively provide diverse guiding sources, thereby maintaining population diversity. Second, in the exploration phase, the dependence on prey is reduced, while in the exploitation phase, both spiral and linear encircling strategies are employed to effectively alleviate the imbalance between exploration and exploitation. Finally, an adaptive dimensional restart mechanism is introduced to reset ineffective dimensions and help the population escape from local optima. Comparative experiments with four GJO variants and seven well-established meta-heuristic algorithms demonstrate that CEGJO achieves the best average performance on at least 79% of the functions in the 100-dimensional CEC 2017 benchmark and on at least 60% of the functions in the 20-dimensional CEC 2020 benchmark. Moreover, CEGJO maintains strong superiority in practical applications, achieving the lowest cost in three complex constrained engineering design problems and reducing the average UAV path length by 16.3% compared with GJO, with the lowest computational overhead. These results further confirm the potential of CEGJO for solving real-world engineering optimization problems. The source code of CEGJO is publicly available at <a href="https://github.com/FullCourage/CEGJO">https://github.com/FullCourage/CEGJO</a>.</p>

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Cooperative elite-guided double-encircling golden jackal optimization algorithm with applications in engineering design and UAV path planning

  • 期刊论文

  • 发表日期：

    2026年1月12日

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  • Yunyi Tan,
  • Jieguang He,
  • Zhiping Peng,
  • Delong Cui,
  • Qirui Li

摘要

To overcome the limitations of the Golden Jackal Optimization (GJO) algorithm in complex high-dimensional landscapes, such as insufficient population diversity, poor balance between exploration and exploitation, and a tendency to fall into local optima, a cooperative elite-guided double-encircling golden jackal optimization (CEGJO) algorithm is proposed in this study. First, the original single-guidance mechanism is removed, and multiple elite groups are introduced to cooperatively provide diverse guiding sources, thereby maintaining population diversity. Second, in the exploration phase, the dependence on prey is reduced, while in the exploitation phase, both spiral and linear encircling strategies are employed to effectively alleviate the imbalance between exploration and exploitation. Finally, an adaptive dimensional restart mechanism is introduced to reset ineffective dimensions and help the population escape from local optima. Comparative experiments with four GJO variants and seven well-established meta-heuristic algorithms demonstrate that CEGJO achieves the best average performance on at least 79% of the functions in the 100-dimensional CEC 2017 benchmark and on at least 60% of the functions in the 20-dimensional CEC 2020 benchmark. Moreover, CEGJO maintains strong superiority in practical applications, achieving the lowest cost in three complex constrained engineering design problems and reducing the average UAV path length by 16.3% compared with GJO, with the lowest computational overhead. These results further confirm the potential of CEGJO for solving real-world engineering optimization problems. The source code of CEGJO is publicly available at https://github.com/FullCourage/CEGJO.