Research on Mission Chain Scheduling Methods for UAV Swarms Using Intelligent Algorithms

As Unmanned Aerial Vehicles (UAVs) evolve toward swarming and intelligence, the challenges brought about by the complexity and uncertainty of rapidly growing UAV swarms have become more pronounced. These challenges significantly affect the efficiency and reliability of swarm mission execution. Thus, the key to UAV swarm technology is determining effective scheduling techniques that assign tasks to individual UAVs, maximize the global mission benefits, and enhance the reliability of swarm missions. This study delves into the modeling of UAV swarm mission chain scheduling issues and the optimization of mission chain scheduling. Initially, utilizing complex network technology, we represent the mission capability of the mission chain from a flow perspective, constructing a flow network model. Subsequently, by employing two intelligent algorithms-the ant colony algorithm and the genetic algorithm-we build optimization objective functions based on the value tradeoff function of the UAV swarm mission chain structure. With mission capability as a constraint, we explore the optimal scheduling strategy for the swarm. We then analyze typical UAV swarm application scenarios as case studies to corroborate the effectiveness and practical feasibility of the proposed methods in engineering practices. Our research lays a foundational framework for decision-making in mission chain scheduling for UAV swarms and analogous systems, ultimately enhancing the reliability of mission execution in related systems.