Railway multi UAV collaborative encirclement strategy based on Grey Wolf optimization dynamic encirclement points

To address the threat of invading drones along railway lines, this paper proposes a multi-UAV cooperative capture strategy based on the Grey Wolf Optimizer (GWO) algorithm and dynamic capture points. Firstly, a motion model in three-dimensional space is established according to the movement characteristics of invading drones along railway lines. Secondly, three-dimensional capture points are dynamically generated based on the movement direction of invading drones, and a negotiation allocation mechanism is designed to achieve optimal matching between capture points and UAVs. Then, an objective function combining path consumption and encirclement effect is constructed, and the GWO algorithm is used to optimize the UAV heading angle increment in real-time. Finally, the effectiveness of the algorithm is verified through three-dimensional simulations. The simulations show that this strategy can achieve efficient capture in three-dimensional environments. Compared with strategies without GWO optimization, the average capture time is reduced by 55.5%, and the capture success rate is improved by 4.8%. Furthermore, in comparison with other mainstream optimization algorithms such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Differential Evolution (DE), our approach yields superior performance in both the average number of capture steps (55.7 steps) and success rate (100%), providing an efficient and reliable technical solution for railway airspace security protection.