Multi-efficiency optimization method of jamming resource based on multi-objective grey wolf optimizer
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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。
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关键词:
- 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.
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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。 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 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 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 -
[1] 王星.航空电子对抗原理[M].北京:国防工业出版社, 2008.WANG X.Principles of electronic countermeasures in aviation[M].Beijing:National Defense Industry Press, 2008(in Chinese). [2] MANZ B.Cognition:EW gets brainy[J].Journal of Electronic Defense, 2012, 35(10):32-39. http://gateway.proquest.com/openurl?res_dat=xri:pqm&ctx_ver=Z39.88-2004&rfr_id=info:xri/sid:baidu&rft_val_fmt=info:ofi/fmt:kev:mtx:article&genre=article&jtitle=Journal%20of%20Electronic%20Defense&atitle=Cognition%3A%20EW%20Gets%20Brainy [3] 范忠亮, 朱耿尚, 胡元奎.认知电子战概述[J].电子信息对抗技术, 2015, 30(1):33-38. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=dzdkjs201501007FAN Z L, ZHU G S, HU Y K.An overview of cognitive electronic warfare[J].Electronic Information Warfare Technology, 2015, 30(1):33-38(in Chinese). http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=dzdkjs201501007 [4] 熊维毅.多平台干扰资源智能调度模型与方法[D].成都: 西南交通大学, 2017: 11-18. http://cdmd.cnki.com.cn/Article/CDMD-10613-1017124985.htmXIONG W Y.Multi-platform jamming resources intelligent scheduling and method[D].Chengdu: Southwest Jiaotong University, 2017: 11-18(in Chinese). http://cdmd.cnki.com.cn/Article/CDMD-10613-1017124985.htm [5] 呙鹏程, 王星, 程嗣怡, 等.应用二次过滤模型的干扰任务分配[J].电讯技术, 2018, 58(2):178-185. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=dianxjs201802011GUO P C, WANG X, CHENG S Y, et al.Implication of secondary filtration model for jamming task allocation[J].Telecommunication Engineering, 2018, 58(2):178-185(in Chinese). http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=dianxjs201802011 [6] 杨远志, 王星, 程嗣怡, 等.基于模糊层次分析法的雷达导引头干扰效能评估[J].火力与指挥控制, 2016, 41(10):10-14. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=hlyzhkz201610004YANG Y Z, WANG X, CHENG S Y, et al.Jamming effectiveness evaluation of radar seeker based on fuzzy analytical hierarchy process[J].Fire Control & Command Control, 2016, 41(10):10-14(in Chinese). http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=hlyzhkz201610004 [7] 刘陈, 刘以安, 薛松.雷达干扰资源优化分配模型和算法研究[J].计算机仿真, 2016, 33(5):23-26. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=jsjfz201605006LIU C, LIU Y A, XUE S.Research of radar jamming resources optimization allocation model and algorithm[J].Computer Simulation, 2016, 33(5):23-26(in Chinese). http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=jsjfz201605006 [8] 李东生, 高杨, 雍爱霞.基于改进离散布谷鸟算法的干扰资源分配研究[J].电子与信息学报, 2016, 38(4):899-905. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=dzkxxk201604019LI D S, GAO Y, YONG A X.Jamming resource allocation via improved discrete cuckoo search algorithm[J].Journal of Electronics & Information Technology, 2016, 38(4):899-905(in Chinese). http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=dzkxxk201604019 [9] 张翔, 吴华, 陈游.基于人工蜂群的协同干扰资源优化方法[C]//2018全国电子战大会论文集.北京: 中国电子学会, 2018: 57-61.ZHANG X, WU H, CHEN Y.Cooperative interference resource optimization method based on artificial bee colony[C]//Proceedings of the 2018 National Electronic Warfare Conference.Beijing: Chinese Institute of Electronics, 2018: 57-61(in Chinese). [10] DEB K, PRATAP A, AGARWAL S, et al.A fast and elitist multi-objective genetic algorithm:NSGA-Ⅱ[J].IEEE Transactions on Evolutionary Computation, 2002, 6(2):182-197. doi: 10.1109/4235.996017 [11] COELLO C A, LECHUGA M.MOPSO: A proposal for multiple objective particle swarm optimization[C]//Proceedings of IEEE Congress on Evolutionary Computation(CEC2002).Piscataway: IEEE Press, 2002: 1051-1056. [12] MEZURA-MONTES E, REYES-SIERRA M, COELLO COELLO C A.Multi-objective optimization using differential evolution: A survey of the stae-of-the-art[C]//Advances in Differential Evolution.Berlin: Springer, 2008: 173-196. [13] ABIDO M A.Enviromenta/economic power dispatch using multi-objective evolutionary algorithms[J].IEEE Transactions on Power Systems, 2003, 18(4):1529-1537. http://ieeexplore.ieee.org/document/1245580 [14] 崔明朗, 杜海文, 魏政磊, 等.多目标灰狼优化算法的改进策略研究[J].计算机工程与应用, 2018, 54(5):156-164. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=jsjgcyyy201805025CUI M L, DU H W, WEI Z L, et al.Research on improved strategy for multi-objective grey wolf optimizer[J].Computer Engineering and Applications, 2018, 54(5):156-164(in Chinese). http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=jsjgcyyy201805025 [15] MIRIJALILI S, MIRJALILI S M, LEWIS A.Grey wolf optimizer[J].Advances in Engineering Software, 2014, 69(3):46-61. http://www.sciencedirect.com/science/article/pii/S0965997813001853 [16] 王晴昊, 姚登凯, 赵顾颢.基于改进灰狼优化算法的电子干扰机空域划设[J].航空工程进展, 2018, 9(3):326-333. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=hkgcjz201803004WANG Q H, YAO D K, ZHAO G H.Improved grey wolf optimization for electronic jamming airspace layout[J].Advances in Aeronautical Science and Engineering, 2018, 9(3):326-333(in Chinese). http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=hkgcjz201803004 [17] 陈辅斌, 李忠学, 杨喜娟.基于改进NSGA2算法的多目标柔性作业车间调度[J].工业工程, 2018, 21(2):55-61. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=gygc201802008CHEN F B, LI Z X, YANG X J.Multi-objective flexible job shop scheduling based on improved NSGA2 algorithm[J].Industrial Engineering Journal, 2018, 21(2):55-61(in Chinese). http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=gygc201802008