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

北京航空航天大学学报 ›› 2020, Vol. 46 ›› Issue (10): 1990-1998.doi: 10.13700/j.bh.1001-5965.2019.0559

• 论文 • 上一篇

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

空军工程大学航空工程学院, 西安 710038

收稿日期:2019-10-29 发布日期:2020-10-29
通讯作者: 陈游 E-mail:chenyousky@163.com
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。

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

XING Huaixi, WU Hua, CHEN You, ZHANG Xiang

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

Received:2019-10-29 Published:2020-10-29

摘要/Abstract

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。

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.

Key words: jamming resource, Multi-Objective Grey Wolf Optimizer (MOGWO), multi-objective optimization, minimizing the battle loss, jamming efficiency

中图分类号:

TN974

邢怀玺, 吴华, 陈游, 张翔. 基于多目标灰狼算法的干扰资源多效能优化方法[J]. 北京航空航天大学学报, 2020, 46(10): 1990-1998.

XING Huaixi, WU Hua, CHEN You, ZHANG Xiang. Multi-efficiency optimization method of jamming resource based on multi-objective grey wolf optimizer[J]. Journal of Beijing University of Aeronautics and Astronautics, 2020, 46(10): 1990-1998.

参考文献

[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.
[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.FAN 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).
[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.XIONG W Y.Multi-platform jamming resources intelligent scheduling and method[D].Chengdu:Southwest Jiaotong University,2017:11-18(in Chinese).
[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.GUO 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).
[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.YANG 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).
[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.LIU 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).
[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.LI 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).
[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.
[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.
[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.CUI 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).
[15] MIRIJALILI S,MIRJALILI S M,LEWIS A.Grey wolf optimizer[J].Advances in Engineering Software,2014,69(3):46-61.
[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.WANG 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).
[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.CHEN 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).

基于多目标灰狼算法的干扰资源多效能优化方法

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

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	王岩, 林彬, 东野广恒, 董颖怀, 赵静楠, 张晓峰. 附件化超声振动工作台设计及有限元优化分析[J]. 北京航空航天大学学报, 2019, 45(8): 1589-1596.
[2]	吴文浩, 张学军, 顾博, 朱晓辉. 基于军民融合的全局飞行流量协同优化方法[J]. 北京航空航天大学学报, 2018, 44(9): 1926-1932.
[3]	徐小明, 张武翔, 丁希仑. 基于模块化的缠绕机设计方法[J]. 北京航空航天大学学报, 2018, 44(4): 746-758.
[4]	杨会, 房海蓉, 李典, 方跃法. 一种新型并联灌注机器人运动学分析和多目标优化[J]. 北京航空航天大学学报, 2018, 44(3): 568-575.
[5]	杜海文, 崔明朗, 韩统, 魏政磊, 唐传林, 田野. 基于多目标优化与强化学习的空战机动决策[J]. 北京航空航天大学学报, 2018, 44(11): 2247-2256.
[6]	姜雨, 徐成, 蔡梦婷, 陈丽丽. 基于双层规划模型的滑行道与停机位再指派联合调度[J]. 北京航空航天大学学报, 2018, 44(11): 2437-2443.
[7]	刘学翱, 吴宏宇, 王春洁, 丁宗茂, 丁建中. 着陆器变阻尼缓冲器性能分析及参数优化[J]. 北京航空航天大学学报, 2018, 44(10): 2149-2155.
[8]	陈云鹏, 庞丽萍, 尹永利, 李国相, 王浚. 空间站大气环控系统应急运行策略优化[J]. 北京航空航天大学学报, 2017, 43(9): 1924-1930.
[9]	吴宏宇, 王春洁, 丁建中, 满剑锋, 罗敏. 基于多工况的新型着陆器软着陆性能优化[J]. 北京航空航天大学学报, 2017, 43(4): 776-781.
[10]	刘坤, 刘明雍, 杨盼盼, 李红, 彭星光. 磁异常干扰下基于约束策略的仿生导航方法[J]. 北京航空航天大学学报, 2016, 42(9): 1952-1957.
[11]	王雷, 赵秋红, 许绍云. 考虑柔性检修计划的圆钢热轧批量调度[J]. 北京航空航天大学学报, 2016, 42(3): 435-443.
[12]	陶杨, 韩维. 基于改进多目标遗传算法的舰尾紊流模拟方法[J]. 北京航空航天大学学报, 2015, 41(3): 443-448.
[13]	顾佼佼, 赵建军, 颜骥, 陈学东. 基于MODPSO-GSA的协同空战武器目标分配[J]. 北京航空航天大学学报, 2015, 41(2): 252-258.
[14]	雷宇曜, 姜文志, 刘立佳, 马向玲. 基于子目标进化的高维多目标优化算法[J]. 北京航空航天大学学报, 2015, 41(10): 1910-1917.
[15]	徐敏, 黄沛霖, 燕瑛, 李五洲. 基于动态指标的飞机方案多目标优化方法[J]. 北京航空航天大学学报, 2014, 40(7): 965-969.