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<p>We propose a multi-strategy Improved Grey Wolf Optimizer (IGWO) to address premature convergence and the imbalance between exploration and exploitation in the conventional Grey Wolf Optimizer (GWO) for three-dimensional (3D) unmanned aerial vehicle (UAV) path planning over complex digital elevation models (DEMs), a problem that can become computationally demanding in large-scale or time-sensitive settings. IGWO incorporates three mechanisms into the standard GWO framework: a nonlinear convergence factor that prolongs early-stage global exploration while accelerating late-stage convergence; a Lévy flight perturbation that enables long-range transitions to help escape local optima; and a Differential Evolution (DE)-based local search that introduces adaptive difference-vector perturbations to enhance fine-grained exploitation. Extensive experiments on the CEC2017 benchmark suite and DEM-based 3D UAV path-planning scenarios demonstrate that IGWO achieves superior overall performance in solution quality and robustness compared with the considered baselines. These results indicate its practical potential for large-scale and computationally demanding UAV path-planning tasks. Although the present study focuses on a sequential metaheuristic framework, the addressed problem involves repeated evaluation of candidate trajectories under complex terrain and multiple constraints, and may therefore benefit from future acceleration on high-performance computing platforms.</p>

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A multi-strategy improved grey wolf optimizer for 3D UAV path planning

  • 期刊论文

  • 发表日期：

    2026年5月6日

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  • Jihong Song,
  • Guangyu Li,
  • Yanxin Li,
  • Xuesong Zhang,
  • Hua Jin,
  • Xiaohong Yan,
  • Xiaopeng Yan

摘要

We propose a multi-strategy Improved Grey Wolf Optimizer (IGWO) to address premature convergence and the imbalance between exploration and exploitation in the conventional Grey Wolf Optimizer (GWO) for three-dimensional (3D) unmanned aerial vehicle (UAV) path planning over complex digital elevation models (DEMs), a problem that can become computationally demanding in large-scale or time-sensitive settings. IGWO incorporates three mechanisms into the standard GWO framework: a nonlinear convergence factor that prolongs early-stage global exploration while accelerating late-stage convergence; a Lévy flight perturbation that enables long-range transitions to help escape local optima; and a Differential Evolution (DE)-based local search that introduces adaptive difference-vector perturbations to enhance fine-grained exploitation. Extensive experiments on the CEC2017 benchmark suite and DEM-based 3D UAV path-planning scenarios demonstrate that IGWO achieves superior overall performance in solution quality and robustness compared with the considered baselines. These results indicate its practical potential for large-scale and computationally demanding UAV path-planning tasks. Although the present study focuses on a sequential metaheuristic framework, the addressed problem involves repeated evaluation of candidate trajectories under complex terrain and multiple constraints, and may therefore benefit from future acceleration on high-performance computing platforms.