基于整数规划和差分进化的多无人机协同烟幕干扰策略优化方法研究

A Hybrid Integer Programming and Differential Evolution Framework for Multi-UAV Cooperative Smoke Screen Jamming Optimization

摘要: 本文针对多导弹威胁背景下无人机集群协同干扰的优化决策问题，提出一种基于整数规划与差分进化的多无人机烟幕干扰策略优化方法。通过建立导弹–无人机多体运动学模型和时空遮蔽分析模型，构建以总有效遮蔽时长最大化为目标的优化函数。采用整数规划完成无人机与导弹的任务分配，并利用差分进化算法对无人机航向角、飞行速度、干扰弹投放时机等多参数进行协同优化。仿真结果表明：经过10,000次迭代优化，该方法可实现总有效遮蔽时长21.11秒，相较于标准粒子群算法提升了14.4%，验证了所提方法在多目标协同干扰中的有效性与优越性，为复杂环境下无人机集群协同防御提供了理论依据与决策支持。

Abstract: This paper addresses the optimization decision-making problem for cooperative jamming by unmanned aerial vehicle (UAV) clusters against multiple missile threats. We propose a multi-UAV smoke screen jamming strategy optimization method based on Integer Programming (IP) and Differential Evolution (DE). By establishing a multi-body kinematics model for the missile-UAV system and a spatio-temporal obscuration analysis model, an optimization function aimed at maximizing the total effective obscuration duration is constructed. The proposed approach employs Integer Programming to accomplish the task assignment between UAVs and missiles, and subsequently utilizes the Differential Evolution algorithm to collaboratively optimize multiple parameters, including the UAVs’ heading angles, flight velocities, and the timing of jamming cartridge deployment. Simulation results demonstrate that after 10,000 iterative optimizations, the proposed method achieves a total effective obscuration duration of 21.11 seconds, which represents a 14.4% improvement compared to the standard Particle Swarm Optimization (PSO) algorithm. This verifies the effectiveness and superiority of the proposed method in multi-target cooperative jamming scenarios, providing a theoretical foundation and decision-making support for cooperative defense of UAV clusters in complex environments.