Adaptive Strategy for the Path Planning of Fixed-Wing UAV Swarms in Complex Mountain Terrain via Reinforcement Learning

Cooperative path planning for multiple Unmanned Aerial Vehicles (UAVs) within complex mountainous terrain presents a unique challenge, characterized by a high-dimensional search space fraught with numerous local optima. Conventional metaheuristic algorithms often fail in such deceptive landscapes due to premature convergence stemming from a static balance between exploration and exploitation. To overcome the aforementioned limitations, this paper develops the Reinforcement Learning-guided Hybrid Sparrow Search Algorithm (RLHSSA), an optimization framework specifically engineered for robust navigation in complex topographies. The core innovation of RLHSSA lies in its two-level architecture. At a lower level, a purpose-built operator suite provides specialized tools essential for mountain environments: robust exploration strategies, including Levy Flight, to escape the abundant local optima, and an Elite-SSA for the high-precision exploitation needed to refine paths within narrow corridors. At a higher level, a reinforcement learning agent intelligently selects the most suitable operator to adapt the search strategy to the terrain’s complexity in real-time. This adaptive scheduling mechanism is the key to achieving a superior exploration–exploitation balance, enabling the algorithm to effectively navigate the intricate problem landscape. Extensive simulations within challenging mountainous environments demonstrate that RLHSSA consistently outperforms state-of-the-art algorithms in solution quality and stability, validating its practical potential for high-stakes multi-UAV mission planning.