【摘要】 【目的】多无人机路径规划是保障无人机群在复杂环境中高效协同作业的关键技术，其本质为可行域稀疏的约束多目标优化问题。进化算法因具备较强的全局搜索能力，已被广泛应用于该类问题的求解。然而，现有方法在路径初始化阶段往往忽略路径的空间结构特征，在路径重生成阶段则主要依赖通用的随机搜索算子，缺乏对适应度函数和无人机与飞行环境空间关系的有效利用，限制了算法在有限计算资源下获取高质量全局可行解的能力。【方法】本文提出一种面向多无人机路径规划的多源启发式进化算法（MSHEA）。该算法通过引入路径空间结构、适应度信息及飞行环境特征等多源启发式信息，分别优化路径初始化与重生成过程：设计按序有向膨胀策略，生成兼具空间合理性和解空间覆盖性的高质量初始路径集合；构建融合适应度与飞行环境信息的路径重生成机制，有效提升不可行路径的修复效率与可行路径的局部优化能力。【结果】基于8组不同复杂度的公开多无人机路径规划基准数据开展实证验证，结果表明MSHEA在不同飞行场景下均展现出优越的求解性能和较高的稳定性：(1)相比次优基准算法，其超体积指标提升1%～6%，反世代距离指标降低6%～81%；(2)所设计的路径初始化策略与重生成机制在提升算法性能方面均发挥了显著作用；(3)对新增超参数的敏感性较低，具有良好的适应性与通用性。【结论】MSHEA通过引入多源启发式信息显著增强了多无人机路径规划的求解能力，为多机协同作业任务中的路径优化问题提供了稳健可靠的技术支撑。更多还原

【Abstract】 [Objectives] Multiple Unmanned Aerial Vehicles(UAVs) path planning is a pivotal technology enabling efficient and cooperative operation of UAV swarms in complex environments. Fundamentally, it constitutes a constrained multi-objective optimization problem characterized by a sparse feasible region. Due to their robust global search capabilities, evolutionary algorithms have been widely adopted to address this class of problems. However, existing methodologies frequently neglect the spatial structural attributes of paths during the initialization phase and primarily rely on generic stochastic search operators for path regeneration. These limitations restrict their ability to effectively exploit the fitness function and the spatial relationships between UAVs and their operational environment, thereby hindering the generation of high-quality, globally feasible solutions under constrained computational resources. [Methods] To address these challenges, this paper introduces a MultiSource Heuristic Evolutionary Algorithm(MSHEA) for multiple UAV path planning. MSHEA systematically integrates multi-source heuristic information related to spatial path structure, fitness data, and environmental context to enhance both the path initialization and regeneration processes. Specifically, we propose a Sequential Directed Expansion-based Initialization(SDEI) strategy to generate high-quality initial paths that exhibit spatial rationality and structural diversity. Furthermore, we develop a path regeneration mechanism integrating fitness and Flight Environment Information(FFEI), which substantially improves the repair efficiency of infeasible paths and the local optimization of feasible ones. [Results] Empirical validation was conducted using eight publicly available benchmark datasets for multiple UAV path planning, covering a range of complexity levels. The experimental results demonstrate MSHEA’s superior performance and elevated stability across diverse flight scenarios:(1) Compared to the suboptimal benchmark algorithm, MSHEA achieved a 1%～6% improvement in hypervolume and a 6%～81% reduction in inverted generational distance.(2) Both the SDEI and FFEI components were confirmed to have significant positive impacts on the algorithm’s overall performance.(3) MSHEA shows low sensitivity to its newly introduced hyperparameters, indicating strong adaptability and generalization capability. [Conclusions] In conclusion, MSHEA improves the effectiveness of solving multiple UAV path planning problems by incorporating multi-source heuristic information. This leads to robust and reliable performance for the challenges inherent in collaborative UAV missions.更多还原