A Computational Framework for Smoke Screen Scheduling in Multi-Agent UAV Systems Using Differential Evolution

This paper proposes a computational and optimization model for determining the effective shielding duration of enemy missiles by smoke countermeasure deployment from unmanned aerial vehicles (UAVs), focusing on the application of a multi-decision variable nonlinear optimization method based on the Differential Evolution (DE) algorithm. First, by analyzing the flat trajectory of smoke countermeasures and the detonation point location, combined with missile trajectory equations and line-of-sight distance criteria, the effective shielding duration is derived. Second, with maximizing shielding duration as the objective, parameters such as heading angle, flight speed, smoke grenade deployment time, and detonation delay are optimized. The DE algorithm performs a global search in continuous variable space through mutation, crossover, and selection operations. Finally, the approach was extended to scenarios involving a single UAV deploying three smoke grenades and a three-UAV deployment. In both cases, the DE algorithm optimized multiple decision variables to derive optimal masking strategies. This model enables precise quantification and optimization of smoke interference effects. The DE algorithm ensures the acquisition of globally optimal solutions, adapts to multi-scenario parameter optimization, and effectively enhances the effectiveness of countermeasures against enemy missiles.