A Two-Stage Multi-UAV Task Allocation Approach Based on Graph Theory and a Learning-Inspired Immune Algorithm

Unmanned aerial vehicle (UAV) systems offer significant advantages in terms of rapid decision-making processes, precise operational execution, and robust performance in complex environments. With the evolution of security paradigms and advancements in equipment systems, collaborative multi-UAV operations have become essential for modern protection frameworks and represent a key developmental direction. To address the need for coordinated multi-objective operations within complex network environments—and to overcome the limitation in current research where single operational loops can only handle individual objectives—this study proposes an innovative intelligent loop recommendation method. By formulating the operational loop recommendation problem as a network flow shortest-path model, we implement an exact algorithm to generate operational loops that enable coordinated multi-UAV operations. Additionally, we develop a learning-inspired algorithm (LIA) incorporating Pareto optimization strategies and specialized learning mechanisms to effectively resolve multi-objective conflicts in UAV task allocation. This research integrates complex network theory with operational loop optimization concepts, providing new technical support for intelligent mission management systems.