Task Scheduling for UAV Swarms with Limited Communication Range

With the widespread adoption of unmanned aerial vehicle （UAV） technology， task scheduling for UAV swarms has become a crucial approach to improve operational efficiency. Most existing studies oversimplify the operational process rules of UAVs， making it difficult to accurately characterize the adaptability differences of UAVs to various tasks under practical operational constraints. To address this limitation， this paper proposes a UAV swarm task scheduling problem with limited communication range （UAVS-LCR） and establishes an integer programming model for its formal description. For solving this problem， a multi-neighborhood iterative local search （MNILS） algorithm is designed， which adopts a doubly linked list solution representation method to reduce the computational complexity of basic neighborhood operations. This algorithm generates high-quality initial solutions via a greedy construction strategy， combines insertion search， multi-swap search and the two-opt operator to enable alternating exploration across multiple neighborhoods， and incorporates a simulated annealing mechanism to balance search efficiency and solution diversity. This method can provide an effective solution for various application scenarios including wide-area UAV inspection and heterogeneous UAV collaborative operations. Experimental results on 12 power grid maintenance test instances demonstrate that the MNILS algorithm significantly outperforms the genetic algorithm， the artificial bee colony algorithm， the ant colony optimization algorithm and the variable neighborhood search algorithm in terms of both solution quality and scalability for large-scale problems.