Research on Unmanned Aerial Vehicles (UAVs) Trajectory Based on Particle Swarm Optimization (PSO) and Wolf Pack Algorithm (WPA)

With the rapid development of drone technology, the application domains for unmanned aerial vehicles (UAVs) have expanded significantly. In practical applications, effective trajectory planning enhances the operational efficiency and safety of UAVs. This paper primarily investigated the application of Particle Swarm Optimization (PSO) and Wolf Pack Algorithm (WPA) in UAV trajectory planning to address the path planning challenges faced by drones in complex environments. Firstly, the fundamental principles of the PSO algorithm and its adaptability in UAV path planning were analyzed. Secondly, the WPA was introduced, exploring how the social hierarchy and cooperative hunting behaviors of wolves could be leveraged to effectively resolve path planning issues. Finally, simulations were conducted using MATLAB to evaluate the performance of both algorithms in various environments and assess their optimization of flight paths. This study not only demonstrates the practicality of PSO and WPA in UAV trajectory planning but also provides robust algorithmic support for future UAV applications.