摘要： 组网雷达干扰资源分配问题是典型的NP问题，同时也是一大难题，需采用各种优化算法对其进行求解，针对传统干扰资源分配优化算法计算速度慢、适应性差的问题，系统梳理了干扰资源分配的智能优化算法的研究进展。首先构建了组网雷达干扰资源分配的数学模型及求解框架，分析了其求解难点，强调智能优化算法在计算效率、全局优化能力及鲁棒性等方面的明显优势；然后以遗传算法、粒子群算法、蚁群算法及其各种改进算法为典型代表，对智能优化算法在组网雷达干扰资源分配中的实施流程、求解效果及优缺点等进行详细分析，并对融合算法及其它仿生/机器学习智能优化算法在该领域的应用进行总结归纳，从适应性、收敛性、全局搜索能力等方面对比分析了各类算法的优劣，充分展现了智能优化算法在该应用方向上的发展现状；最后结合当前组网雷达干扰资源分配所面临的多重挑战，从算法对比、寻优速度、融合创新与动态适应性四个方面对智能优化算法未来的发展方向做出了展望，对组网雷达干扰资源分配中智能优化算法的研究及工程实践具有重要的参考价值。

Abstract: The networked radar jamming resource allocation is a typical NP-hard problem and also a significant challenge, requiring the use of various optimization algorithms to solve it. To address the issues of slow computational speed and poor adaptability in traditional jamming resource allocation optimization algorithms, progress of intelligent optimization algorithms in this field was reviewed. Firstly, a mathematical model and solution framework for networked radar jamming resource allocation were constructed, the difficulties in solving this model were analyzed, and the obvious advantages of intelligent optimization algorithms in terms of computational efficiency, global optimization capability and robustness were emphasized. Then, taking the genetic algorithm, particle swarm optimization, ant colony optimization, and the various improved algorithms as typical examples, the implementation processes, solution effectiveness, and strengths and weaknesses of intelligent optimization algorithms in networked radar jamming resource allocation were analyzed in detail. Additionally, the application of fusion algorithms and other bionic/machine learning-based intelligent optimization algorithms in this field was summarized, the advantages and disadvantages of various algorithms were compared and analyzed from aspects such as adaptability, convergence, and global search capability, fully demonstrating the current development status of intelligent optimization algorithms in this application direction. Finally, combined with the multiple challenges currently encountered in networked radar jamming resource allocation, future development directions of intelligent optimization algorithms were prospected from four aspects: algorithm comparison, optimization speed, fusion innovation and dynamic adaptability. It provides a valuable reference for the research and engineering practice of intelligent optimization algorithms in networked radar jamming resource allocation.