基于MODPSO-GSA的协同空战武器目标分配

顾佼佼; 赵建军; 颜骥; 陈学东

doi:10.13700/j.bh.1001-5965.2014.0119

基于MODPSO-GSA的协同空战武器目标分配

doi: 10.13700/j.bh.1001-5965.2014.0119

1.
海军航空工程学院科研部, 烟台 264001
2. 91352 部队, 威海 264208

基金项目: 国家自然科学基金资助项目(61102167, 61105165); 青年科学基金资助项目(61002006); 航空科学基金资助项目(20135184008)

详细信息

作者简介:
顾佼佼(1986—), 男, 山东青岛人, 博士生, vxgu86@hotmail.com

通讯作者:
赵建军(1965—), 男, 江苏南通人, 教授, zjj@hotmail.com, 主要研究方向为海军武器装备攻防体系对抗与信息化.

中图分类号: V247.1⁺3;E837
计量
- 文章访问数: 1319
- HTML全文浏览量: 144
- PDF下载量: 640
- 被引次数: 0
出版历程
- 收稿日期: 2014-03-12
- 网络出版日期: 2015-02-20

Cooperative weapon-target assignment based on multi-objective discrete particle swarm optimization-gravitational search algorithm in air combat

1.
Department of Scientific Research, Naval Aeronautical and Astronautical University, Yantai 264001, China
2. The 91352 Army, Weihai 264208, China

摘要

摘要: 提出基于多目标决策理论的协同空战武器目标分配模型,并用进化多目标优化算法求解.空战是一个多阶段攻防过程,针对多数空战武器目标分配采用一次性完全分配、不考虑火力资源消耗等不足,构建多目标决策模型,在达到毁伤门限的前提下,同时对一次攻击后使敌编队的总期望剩余威胁最小和分配导弹消耗量最小两个目标函数寻优.提出用多目标离散粒子群-引力搜索算法(MODPSO-GSA)求解分配模型,该混合进化多目标优化算法结合二者优点,具有稳定的全局搜索能力并保证收敛到Pareto前沿.该算法可求得满足毁伤门限的不同耗弹量的分配方案最优解集以供指挥员决策参考.仿真算例验证了新模型及所提出MODPSO-GSA进化多目标优化求解算法的有效性.
- 目标分配 /
- 多目标决策 /
- 进化多目标优化 /
- 粒子群 /
- 引力搜索 /
- Pareto前沿
Abstract: An air combat weapon-target assignment (WTA) model based on multi-objective decision theory with a hybrid evolutionary multi-objective optimization algorithm solver was proposed. Air combat is a multi-stage process of attack-defense countermeasure, existing WTA models are based on disposable fully allocated assignment without considering the missile consumption, which does not conform to the actual air combat process. The minimum of total expected remaining threats and total consumption of missiles were selected as two objectives functions of the multi-objective decision model, with the premise of reaching damage threshold. The hybrid multi-objective discrete particle swarm optimization-gravitational search algorithm (MODPSO-GSA) was proposed to handle the model, which possesses stable global search capacity and promises to converge to Pareto frontier. A Pareto optimal solution set with damage threshold met can be obtained, which offers decision reference to the commander. Simulation results verify that the model is of advantage and the proposed MODPSO-GSA is effective.
- weapon target assignment /
- multi-objective decision making /
- evolutionary multi-objective optimization /
- particle swarm optimization /
- gravitational search /
- Pareto frontier

HTML全文

参考文献(16)

[1]	刘传波, 邱志明,吴玲,等.动态武器目标分配问题的研究现状与展望[J].电光与控制,2010,17(11):43-47. Liu C B,Qiu Z M,Wu L,et al.Review on current status and prospect of researches on dynamic weapon target assignment[J].Electronics Optics & Control,2010,17(11):43-47(in Chinese).
[2]	陈闽. 编队协同作战目标分配建模综述[J].电光与控制,2013,20(9):53-63. Chin M.A survey on modeling of target allocation for formation cooperative combat[J].Electronics Optics & Control,2013,20(9): 53-63(in Chinese).
[3]	李俨, 董玉娜.基于SA-DPSO混合优化算法的协同空战火力分配[J].航空学报,2010,31(3):626-631. Li Y,Dong Y N.Weapon-target assignment based on simulated annealing and discrete particle swarm optimization in cooperative air combat[J].Acta Aeronautica et Astronautica Sinca,2010,31(3): 626-631(in Chinese).
[4]	公茂果, 焦李成,杨咚咚,等.进化多目标优化算法研究[J].软件学报,2009,20(2):271-289. Gong M G,Jiao L C,Yang D D,et al.Evolutionary multi-objective optimization algorithms[J].Journal of Software,2009,20(2): 271-289(in Chinese).
[5]	岳超源. 决策理论与方法[M].北京:科学出版社,2006:151-169. Yue C Y.Decision theory and methods[M].Beijing:Science Press,2006:151-169(in Chinese).
[6]	段海滨. 蚁群算法原理及其应用[M].北京:科学出版社,2006:312-317. Duan H B.Ant colony algorithms: theory and applications[M].Beijing:Science Press,2006:312-317(in Chinese).
[7]	Coello C C, Veldhuizen V D,Lamont G.Evolutionary algorithms for solving multi-objective problems[M].2nd ed.New York:Springer-Verlag,2007:1-10.
[8]	段海滨,张翔银, 徐春芳.仿生智能计算[M].北京:科学出版社,2011:63-85. Duan H B,Zhang X Y,Xu C F.Bio-inspired computing[M].Beijing:Science Press,2011:63-85(in Chinese).
[9]	郭文忠,陈国龙. 离散粒子群优化算法及其应用[M].北京:清华大学出版社,2012:13-16. Guo W Z,Chen G L.Discrete particle swarm optimization algorithm and its application[M].Beijing:Tsinghua University Press,2012:13-16(in Chinese).
[10]	Esmat R, Hossein N,Saeid S.GSA: a gravitational search algorithm[J].Information Sciences,2009,179(13):2232-2248.
[11]	杨晶,黎放, 狄鹏.免疫万有引力搜索算法的研究与仿真[J].兵工学报,2012,33(12):1533-1538. Yang J,Li F,Di P.Research and simulation for the gravitational search algorithms with immunity[J].Acta Armamentarh,2012,33(12):1533-1538(in Chinese).
[12]	谷文祥,李向涛, 朱磊,等.求解流水线调度问题的万有引力搜索算法[J].智能系统学报,2010,5(5):411-418. Gu W X,Li X T,Zhu L,et al.A gravitational search algorithm for flow shop scheduling[J].CAAI Transactions on Intelligent Systems,2010,5(5):411-418(in Chinese).
[13]	李沛,段海滨. 基于改进万有引力搜索算法的无人机航路规划[J].中国科学:技术科学,2012,42(10):1130-1136. Li P,Duan H B.Path planning of unmanned aerial vehicle based on improved gravitational search algorithm[J].Scientia Sinica Technologica,2012,42(10):1130-1136(in Chinese).
[14]	Hadi N, Mahdi N,Patrick S.A multi-objective gravitational search algorithm based on non-dominated sorting[J].International Journal of Swarm Intelligence Research,2012,3(3):32-49.
[15]	Seyedali M, Siti Z M H.A new hybrid PSOGSA algorithm for function optimization[C]//International Conference on Computer and Information Application.Piscataway,NJ:IEEE,2010:374-377.
[16]	Kalyanmoy D, Amrit P,Sameer A,et al.A fast elitist multi-objective genetic algorithm:NSGA-II[J].IEEE Transactions on Evolutionary Computation,2002,6(2):182-197.