Research on Hybrid Intelligent Optimization Methods for Smoke Jammer Effectiveness in Single-UAV Multi-Payload Coordination

Abstract

This paper aims to design an optimal strategy for a single FY1 drone to execute smoke countermeasures against an incoming M1 missile. First, three-dimensional kinematic models of the missile, drone, and smoke grenade were constructed. Using triple geometric criteria—line-of-sight distance—trajectory crossing—points within the cloud—the effective shielding duration of a single grenade under fixed parameters was calculated to be approximately 1.42 seconds. Second, for the optimal deployment strategy of a single smoke grenade, the Particle Swarm Optimization (PSO) algorithm is employed for global search. Setting the decision variable dimension to 4 dimensions significantly increases the optimal masking duration to 4.62 seconds. Finally, for the coordinated deployment of three smoke-interference grenades, the model was expanded to an 8-dimensional decision variable space. A hybrid optimization framework combining differential evolution with simulated annealing (basinhopping/L-BFGS-B) was introduced to address the non-smoothness and multi-modal nature of the objective function. This hybrid strategy fully accounted for constraints like a 1-second deployment interval, ultimately achieving a total effective masking duration of 9.820 seconds—over 110% longer than the single-deployment optimal solution. This demonstrates the synergistic potential of multi-deployment coordination in complex adversarial scenarios.