改进灰狼算法助无人机在复杂环境规划更短路径
An Improved Grey Wolf Optimizer Inspired by Advanced Cooperative Predation for UAV Shortest Path Planning
- 引入高级协作捕食机制与镜像反向学习策略提升优化能力
- 在多个测试函数中表现最优，路径平均比传统算法短1.68~2.00米
- 适合需要高效路径规划的无人机应用，如救援与物流
随着无人机在军事侦察、应急救援和物流配送等领域的广泛应用，高效规划最短飞行路径成为关键挑战。传统启发式方法常陷入局部最优，难以找到全局最优路径。为此，本文提出一种改进灰狼优化器（IGWO），融合高级协作捕食（ACP）与透镜反向学习策略（LOBL），以增强算法优化能力。仿真结果表明，IGWO在基准函数F1-F5、F7及F9-F12上性能最优，优于所有对比算法。进一步应用于多种障碍物环境下的无人机最短路径规划，结果显示，相较于GWO、PSO和WOA，IGWO规划路径平均分别缩短1.70米、1.68米和2.00米，覆盖四张不同地图。
原文摘要 · Abstract (English)
With the widespread application of Unmanned Aerial Vehicles (UAVs) in domains like military reconnaissance, emergency rescue, and logistics delivery, efficiently planning the shortest flight path has become a critical challenge. Traditional heuristic-based methods often suffer from the inability to escape from local optima, which limits their effectiveness in finding the shortest path. To address these issues, a novel Improved Grey Wolf Optimizer (IGWO) is presented in this study. The proposed IGWO incorporates an Advanced Cooperative Predation (ACP) and a Lens Opposition-based Learning Strategy (LOBL) in order to improve the optimization capability of the method. Simulation results show that IGWO ranks first in optimization performance on benchmark functions F1-F5, F7, and F9-F12, outperforming all other compared algorithms. Subsequently, IGWO is applied to UAV shortest path planning in various obstacle-laden environments. Simulation results show that the paths planned by IGWO are, on average, shorter than those planned by GWO, PSO, and WOA by 1.70m, 1.68m, and 2.00m, respectively, across four different maps.
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