Optimization of Smoke Screen Interference Munition Deployment Strategy Based on Improved Particle Swarm and NSGA-II Algorithms

Abstract

To address the threat posed by precision-guided missiles to ground targets in modern warfare, this study focuses on optimizing deployment strategies for smoke screen interference bombs using unmanned aerial vehicles . The research examines two typical scenarios: single-drone-single-bomb and multi-drone-multi-bomb operations. A geometric shielding model based on Newtonian kinematics is established to determine the intersection conditions between smoke cloud clusters and missile-target line-of-sight, enabling precise calculation of shielding duration. For single-drone scenarios, an improved Particle Swarm Optimization algorithm is employed to optimize flight direction, speed, smoke deployment timing, and detonation delay, with the primary goal of maximizing real target shielding duration. In multi-drone scenarios, a multi-objective optimization model is developed to maximize shielding time while minimizing ammunition consumption and enhancing uniformity of coverage, incorporating the military deception effect of decoy targets. The collaborative deployment strategy is solved using the Non-Stochastic Genetic Algorithm-Ⅱ. The proposed model and strategies provide valuable references for practical applications of UAV smoke screen interference technology.