Research on Dynamic Particle Swarm Optimization for Multi-Objective Reconnaissance Task Allocation of UAVs

With the increasingly widespread application of unmanned aerial vehicle (UAV) systems in disaster monitoring, urban management, logistics transportation, and reconnaissance, efficient dynamic task allocation has become a key issue in improving task execution efficiency. To address the challenges posed by dynamic changes in task objectives and resource constraints that traditional task allocation methods struggle with in complex environments, this paper proposes a multi-objective particle swarm optimization algorithm, DCMPSO, for UAV dynamic reconnaissance task allocation. First, the framework of DCMPSO is constructed, dividing the optimization of dynamic problems into three parts: environment change detection, environment change response, and actual optimization, with the designed strategy of range prediction strategy based on centroid translation. Then, simulation experiments are conducted to verify the effectiveness of the algorithm mechanisms through ablation experiments and to demonstrate the superiority of DCMPSO in convergence and distribution compared to DNSGA-II and SGEA through comparative experiments. Finally, a multi-UAV dynamic task allocation model is established and optimized, proving that DCMPSO can correctly solve the UAV dynamic multi-objective allocation problem and effectively find its optimal solution, providing an effective solution for practical applications.