In response to the challenges of complex terrain, widely distributed task points, and low efficiency in task allocation and path planning for Unmanned Aerial Vehicle (UAV) power inspection tasks in large mountainous areas, a hybrid optimization algorithm based on the black-winged kite and Arctic puffin optimizer (HBAO) is proposed, which can coordinate task allocation and path planning of UAV. First, an optimization objective function is established based on constraints such as total flight distance, average flight altitude, and terrain threats. Next, an improved distance-weighted random step size search strategy is employed to enhance the predation phase of the Black-winged Kite Algorithm, strengthening the algorithm’s global search capability. Then, a Fitness and Distance-Based (FDB) strategy for optimal individual selection is introduced to improve the global search efficiency and optimization accuracy of the Black-winged Kite Algorithm during the migration phase. Finally, the cooperative hunting mechanism of the Arctic Puffin Algorithm is incorporated, allowing for individual collaboration in updating positions, which can effectively enhance the algorithm’s ability to escape from local optima and ensures diversity and efficiency in global search. Simulations conducted using a Digital Elevation Model (DEM) of the Qinling Mountains demonstrate that, in scenarios with numerous inspection task points, the overall performance of the proposed HBAO outperforms that of six comparison algorithms, significantly reducing global costs.

针对大范围山地环境下无人机电力巡检任务中地形复杂、任务点分布范围大以及任务分配和路径规划效率低的问题, 提出一种基于黑翅鸢–北极海雀的混合优化器(HBAO), 实现无人机任务分配和路径规划的协同优化。首先, 根据总飞行距离、平均飞行高度和地形威胁等约束条件, 建立优化目标函数。然后, 通过改进基于距离权重的随机步长搜索策略, 优化黑翅鸢算法的捕食阶段, 增强算法的全局搜索能力。再后, 引入基于适应度和距离的最优个体选择(FDB)策略, 强化黑翅鸢算法在迁徙阶段的全局搜索效率和优化精度。最后, 引入北极海雀算法的合作捕食机制, 通过个体协作来更新位置, 有效地提升算法跳出局部最优的能力, 确保全局搜索的多样性和搜索效率。选取秦岭局部地区的数字高程模型(DEM)进行仿真实验, 结果表明, 在巡检任务点繁多的情况下, 基于黑翅鸢–北极海雀混合优化算法的综合性能优于6种对比算法, 且全局代价显著降低。