Multiloitering Munition Cooperative Surrounding and Attack Method Based on Wolf Pack Algorithm

Aiming at the optimization problem of multiloitering munitions cooperatively surrounding moving targets, this paper proposes a cooperative surrounding and attack method based on the wolf pack algorithm (WPA). Firstly, the relative kinematics model between the munitions and the target is established, and the cooperative surrounding task flow is analyzed in detail. Secondly, leveraging the “wandering–summoning–besieging” mechanism of the WPA, a comprehensive fitness function incorporating multiple constraints is designed, including favorable attack radius, attack angle constraint, surrounding area constraint, intermunition collision avoidance, and desired surrounding points. This method utilizes the WPA to distributively solve for the optimal control command in each decision cycle, guiding the munition cluster to maneuver cooperatively and achieve an even distribution around the target. Simulation results demonstrate that compared with other swarm intelligence algorithms such as particle swarm optimization (PSO), grey wolf optimizer (GWO), and sparrow search algorithm (SSA), the proposed method exhibits superior performance in terms of convergence speed, solution accuracy (fitness value), and computational efficiency (CPU time). It effectively achieves cooperative surrounding and attack against both single and multiple targets, significantly enhancing combat effectiveness and providing a novel solution for the cooperative decision‐making of intelligent munition clusters.