基于多目标灰狼算法的干扰资源多效能优化方法

邢怀玺; 吴华; 陈游; 张翔

doi:10.13700/j.bh.1001-5965.2019.0559

基于多目标灰狼算法的干扰资源多效能优化方法

doi: 10.13700/j.bh.1001-5965.2019.0559

空军工程大学航空工程学院, 西安 710038

详细信息

作者简介:
邢怀玺男, 硕士研究生。主要研究方向:信息对抗理论与技术

陈游男, 博士, 讲师。主要研究方向:信息对抗理论与技术

通讯作者:
陈游, E-mail: chenyousky@163.com

中图分类号: TN974
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- 被引次数: 0
出版历程
- 收稿日期: 2019-10-29
- 录用日期: 2020-02-21
- 网络出版日期: 2020-10-20

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

Aeronautics Engineering College, Air Force Engineering University, Xi'an 710038, China

More Information

Corresponding author: CHEN You, E-mail: chenyousky@163.com

摘要

摘要:
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。
- 干扰资源决策 /
- 多目标灰狼算法(MOGWO) /
- 多目标优化 /
- 最小化战损 /
- 干扰效能
Abstract:
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.
- jamming resource /
- Multi-Objective Grey Wolf Optimizer (MOGWO) /
- multi-objective optimization /
- minimizing the battle loss /
- jamming efficiency

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图 1 灰狼等级分类及其主要职责

Figure 1. Grey wolf classification and its main duties

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图 2 拥挤度示意图

Figure 2. Schematic diagram of congestion degree

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图 3 MOGWO算法流程图

Figure 3. Flowchart of MOGWO algorithm

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图 4 基于MOGWO算法的干扰资源调度分配解集

Figure 4. Disposal set of jamming resource allocation based on MOGWO

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图 5 基于NSGA-Ⅱ算法的干扰资源调度分配解集

Figure 5. Disposal set of jamming resource allocation based on NSGA-Ⅱ algorithm

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表 1 机群编队情况

Table 1. Fleet formation information

干扰机ID	平台类型^*
J₁	a
J₂	b
J₃	c
J₄	b
J₅	c
J₆	d
J₇	d
J₈	a
J₉	b
J₁₀	d
注：*表示干扰机平台类型：a~d依次表示轰炸机、战斗机、专用电子干扰机和无人干扰机。

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表 2 非劣解对应的多个目标函数值

Table 2. Multiple objective function values corresponding to non-inferior solutions

方案	接收端干扰总功率	期望作战损失	整体干扰效能
1	17.001 5	2.746 5	3.081 8
2	14.181 8	2.758 7	3.084 1
3	10.623 5	3.697 5	2.645 6
4	3.871 2	3.032 7	2.481 5
5	11.344 8	2.779 4	3.071 0
6	12.870 4	2.959 3	2.730 7
7	7.917 4	3.457 7	2.523 8
8	8.886 0	3.495 6	3.051 4
9	22.565 4	2.691 7	2.831 5
10	6.108 1	3.572 8	3.137 9

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表 3 干扰机分配方案

Table 3. Distribution schemes of jammers

方案	J₁	J₂	J₃	J₄	J₅	J₆	J₇	J₈	J₉	J₁₀
1	R₇	R₅	R₉	R₂	R₄	R₁	R₃	R₁₀	R₈	R₆
2	R₃	R₁	R₅	R₂	R₇	R₁₀	R₉	R₄	R₆	R₈
3	R₉	R₄	R₃	R₁	R₅	R₁₀	R₇	R₆	R₈	R₂
4	R₇	R₁	R₁₀	R₈	R₄	R₉	R₆	R₅	R₂	R₃
5	R₈	R₆	R₂	R₁₀	R₉	R₁	R₄	R₃	R₅	R₇
6	R₁₀	R₅	R₆	R₉	R₂	R₃	R₇	R₄	R₈	R₁
7	R₈	R₆	R₄	R₁	R₅	R₂	R₇	R₃	R₉	R₁₀
8	R₇	R₅	R₁	R₃	R₆	R₂	R₁₀	R₉	R₈	R₄
9	R₂	R₅	R₈	R₁₀	R₄	R₁	R₆	R₉	R₃	R₇
10	R₁₀	R₁	R₉	R₅	R₈	R₃	R₆	R₇	R₂	R₄

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表 4 不同算法IGD值对比

Table 4. Comparison of IGD values among different algorithms

IGD	NSGA-Ⅱ	MOPSO	MOGWO
均值	1.62	2.14	1.48
方差	1.16	0.98	0.92
最优值	0.66	0.72	0.72
最差值	4.68	4.22	3.14

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