基于多目标灰狼算法的干扰资源多效能优化方法

依靠经验决策或简单的模板匹配的传统干扰资源决策方式难以适应当前复杂的电磁环境。针对雷达干扰资源决策的智能化需求展开研究，将干扰资源调度建模为多目标优化问题，以最大化整体干扰效能、最小化干扰总功率、最小化作战损失为目标函数建立干扰资源调度模型，利用一种多目标灰狼算法（MOGWO）求解问题模型Pareto前沿，以最优解集代替最优解，再根据战场实际情况选择最佳调度方案，使决策方案更加科学合理。实验结果表明，MOGWO算法能够克服基本灰狼算法（GWO）探索能力不足、局部收敛的缺陷，有较高的搜索效率，算法的寻优能力和稳定性均优于NSGA-Ⅱ算法和MOPSO算法。 

Multi-efficiency optimization method of jamming resource based on multi-objective grey wolf optimizer

Traditional jamming resource decision-making methods that rely on empirical decisions or simple template matching are difficult to adapt to the current complex electromagnetic environment. This paper focuses on the intelligent requirements of radar jamming resource decision-making. The jamming resource scheduling is modeled as a multi-objective optimization problem, and the jamming resource scheduling model is established with the objective functions of maximizing the overall jamming efficiency, minimizing the total jamming power, and minimizing the battle loss. A Multi-Objective Grey Wolf Optimizer (MOGWO) is used to solve the Pareto front of the problem model. The optimal solution set is used instead of the optimal solution, and then the optimal scheduling scheme is selected according to the actual situation of the battlefield to make the decision scheme more scientific and reasonable. The experimental results show that the MOGWO algorithm can overcome the shortcomings of the basic Grey Wolf Optimizer (GWO), such as lack of exploration competence and local convergence, and has higher search efficiency. The optimization ability and stability of the algorithm are better than those of the NSGA-Ⅱ algorithm and the MOPSO algorithm.