To counter the threat posed by incoming missiles to critical military targets, this study systematically analyzes optimization strategies for deploying smoke-generating decoys via unmanned aerial vehicles (UAVs). Considering that smoke-screen performance is closely related to the physical characteristics of dispersed particulate media, which in some applications may involve fibrous or textile-derived materials, the research first constructs a three-dimensional kinematic model to precisely describe the relative spatial trajectories of missiles, UAVs, and smoke clouds, establishing the physical boundaries for effective concealment using analytical geometric criteria. Building upon foundational effectiveness evaluations, the study introduces a simulated annealing algorithm for global optimization of flight direction, velocity, and deployment timing in single-platform interception tasks, significantly enhancing the shielding efficacy of individual munitions. Subsequently, addressing higher-dimensional challenges of multi-munition coordination and multi-UAV formation operations, a genetic algorithm-based optimization framework is established. This framework achieves precise temporal and spatial sequencing of multiple smoke grenades through a time-window matching model. Particularly in complex decision scenarios involving simultaneous missile attacks, the model synchronously schedules the flight paths and payload timing of five UAVs using high-dimensional decision variables, ensuring maximum coverage during multi-target interception missions. Simulation results demonstrate that this hierarchical, progressive optimization strategy provides scientific quantitative support for defense decisions in complex battlefield environments.

fibrous particulate medium, smoke jammer, coordinated deployment strategy, heuristic algorithm