ADSESC: an Enhanced Escape Algorithm Based on Adaptive Decentralized Search Strategy for Low-altitude UAV Path Planning in Complex Environments

In low-altitude freight logistics scenarios, Unmanned Aerial Vehicle (UAV) path planning plays a critical role in ensuring operational efficiency and collision-free navigation. The Escape algorithm (ESC) represents a promising metaheuristic approach for addressing UAV path planning challenges. However, due to its core limitations of a static fixed-ratio population partitioning strategy and a single search scheme during the late iteration phase, the search behavior of individuals within the population lacks clear guidance and falls into blind exploration. This limits ESC to achieving ideal optimization results only in simple scenarios; when applied to complex UAV navigation environments, its drawback of inadequate solution accuracy will be thoroughly exposed. To mitigate these drawbacks, this study presents an adaptive decentralized search strategy designed to effectively mitigate ESC’s shortcomings. Specifically, the strategy dynamically partitions the population during iterations. Using the average fitness value as a threshold, it divides the population into the elite group and the general group, and assigns differentiated search strategies to each for co-evolution. By integrating this strategy, we develop a novel algorithm referred to as ADSESC. ADSESC is evaluated against other state-of-the-art algorithms on 29 IEEE CEC 2017 benchmark functions and the UAV path planning models based on four constraints. The results demonstrate that ADSESC not only excels in performance across the 29 benchmark functions but also generates smoother and shorter paths compared to the other algorithms evaluated.