Research on UAV Cooperative Air Combat Route Planning Based on Multi-objective Particle Swarm Optimization

With the development and application of UAV technology, collaborative combat route planning with UAVs is attracting more and more attention. This paper studies the UAV cooperative air combat route planning problem based on multi-objective particle swarm optimization(MOPSO), aiming to provide an effective, safe, and reliable means of UAV combat coordination. First, the basic principle and steps of the particle swarm optimization algorithm are introduced as the definition and evaluation index of multi-objective optimization. An improved MOPSO is proposed. By introducing non-dominated sorting, crowding distance, and mutation operation, the convergence and diversity of the algorithm are improved. Second, the UAV cooperative air combat route planning model is established, including flight model and constraints, assignment of missions and target functions, and route generation and smoothing. The UAV cooperative air combat route planning problem is transformed into a multi-objective optimization problem. Finally, the effectiveness and superiority of the proposed algorithm and model are verified through experimentation and analysis. Compared with other comparison algorithms, it can get a better route map depending on satisfying constraints and has better stability and robustness.