Research on the Optimization Model Based on the Deployment Strategy of Smoke Screen Jamming Bombs

To address the complex strategic optimization problem of maximizing target obscuration time through UAV-deployed smoke interference munitions, this study proposes a hybrid intelligent optimization algorithm that integrates Differential Evolution (DE) and Genetic Algorithm (GA). The algorithm was designed to efficiently solve the high-dimensional, nonlinear, and strongly coupled decision space formed by UAV flight states and smoke munition deployment parameters (such as speed, heading, timing of deployment, and fuze delay). Its core mechanism lies in leveraging the powerful global exploration capability of the DE algorithm to quickly identify high-potential solution spaces across a broad range, thereby avoiding the local optima. This is combined with the outstanding local search capability of the GA algorithm to deeply mine and finely optimize the elite solution set, achieving both precision and robustness in the final solution. The algorithm undergoes iterative optimization on a high-fidelity simulation platform that integrates multiagent kinematics and geometric obscuration assessment. The simulation results strongly demonstrate the superiority of the algorithm: compared with benchmark schemes, the optimized single-munition strategy increases the effective obscuration time by 241.7%. In complex multi-UAV cooperative missions, the algorithm was able to devise operational plans with a collaboration efficiency as high as 85.2%, achieving continuous obscuration for 11.87 s. This study confirms that the proposed DE-GA hybrid algorithm is an effective solution for complex military operational optimization problems, providing key algorithmic support for unmanned intelligent combat decision-making.