战术导弹技术

针对现代电子战环境下雷达对抗侦察任务分配的高维、动态、多约束与多目标优化难题，本文提出一种滚动窗口自适应混合混沌粒子群优化算法(Rolling window-adaptive hybrid chaotic particle swarm optimization，RHACPSO)。构建了综合考虑侦察收益、资源匹配度、风险代价与时效性的多目标优化模型，采用滚动时间窗口机制应对战场环境的动态不确定性。设计了领域知识驱动的混合初始化与搜索引导策略，将侦察效能评估模型深度嵌入算法流程。在此基础上，引入多样性感知的自适应混沌扰动机制与轻量级遗传-模拟退火混合操作，平衡全局探索与局部开发能力。仿真实验表明，相较于自适应混沌粒子群算法(Chaotic particle swarm optimization，CPSO)及遗传算法(Genetic algorithm，GA)，本文算法在静态优化精度、动态响应速度以及方案鲁棒性方面均表现出显著优势，能够为复杂电磁环境下的雷达对抗侦察智能决策提供有效支撑。

Aiming at the high-dimensional， dynamic， multi-constraint， and multi-objective optimization challenge in radar counter-reconnaissance task allocation under modern electronic warfare environments， a RHACPSO is proposed. A multi-objective optimization model is constructed， which comprehensively considers reconnaissance benefit， resource matching degree， risk cost， and timeliness. A rolling time window mechanism is employed to handle the dynamic uncertainties of the battlefield environment. A domain knowledge-driven hybrid initialization and search guidance strategy is designed， deeply embedding the reconnaissance effectiveness evaluation model into the algorithm flow. Furthermore， a diversity-aware adaptive chaotic perturbation mechanism and a lightweight genetic-simulated annealing hybrid operation are introduced to balance global exploration and local exploitation capabilities. Simulation experiments demonstrate that， compared to the adaptive CPSO and the GA， the proposed algorithm exhibits significant advantages in static optimization accuracy， dynamic response speed， and solution robustness， providing effective support for intelligent decision-making in radar counter-reconnaissance within complex electromagnetic environments.