UAV Flight and Deployment Strategies Based on a Cooperative-Guidance and Global- Perturbation Particle Swarm Optimization Algorithm

In multi-missile attack scenarios, UAV swarms face a challenging cooperative decision-making problem when deploying smoke munitions to interfere with missile guidance systems. Focusing on the maximization of effective visual obscuration duration, this paper investigates the coordinated planning of UAV flight trajectories and smoke munition deployment strategies. First, the three-dimensional motion and interaction processes of UAVs, missiles, and smoke munitions are modeled in the time domain, and a high-dimensional nonlinear cooperative optimization model is established. Then, to address the dimensional explosion and local optimum issues encountered in model solving, as well as the difficulty of handling simulation-based, non-analytical objective functions, an improved particle swarm optimization algorithm integrating a cooperative-guidance mechanism and a global-perturbation strategy is developed. Combined with threatlevel assessment and a greedy resource allocation strategy, a hierarchical solution framework is proposed. Simulation results demonstrate the effectiveness of the proposed method, showing that it can efficiently generate optimal resource allocation schemes and cooperative UAV interference strategies, achieve superior interference performance in complex adversarial scenarios, and exhibit good engineering applicability and potential for practical deployment.