基于正交遗传算法和灵敏度分析的体系仿真优化方法
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摘要
体系对抗已成为当今战争的主要形式,因而急需对体系优化问题进行深入细致地研究.鉴于此,提出了一种基于正交遗传算法和灵敏度分析的体系仿真优化方法.该方法采用正交遗传算法在可行域内快速地搜索一些较优方案(解);利用灵敏度分析方法从已评估方案中得到待研究体系输入、输出之间的灵敏度关系;应用这种灵敏度关系来指导正交遗传算法的后续搜索.最后采用一些数据实例来验证该方法,实验结果表明,该方法是可行的、正确的和有效的.该方法能在较少次数的仿真后,得到待研究体系的满意解或最优解;同时,该方法可推广到其它复杂优化问题的求解中.
system-of-systems antagonism has already become the dominating modality of modern warfare,so it is urgent to study the optimization problem of system-of-systems.For this purpose,it presents a simulation optimization approach for system-of-systems based on orthogonal genetic algorithm and sensitivity analysis.In the proposed approach,it applies the orthogonal genetic algorithm to find some better solutions from the whole feasible space at first;it uses the sensitivity analysis method to obtain the input-output relationship of system-ofsystems by analyzing the evaluated solutions at second;it adopts the sensitivity relations to guide the subsequent heuristic searching of the orthogonal genetic algorithm at third.Many numerical examples were applied to validate the proposed approach.The optimization result suggests that,the proposed approach is feasible,correct and valid.The proposed approach can achieve the optimum solution(or near-optimum solution) of system-of-systems by executing a few times simulation.This approach can be popularized to solve other complex optimization problems.
system-of-systems antagonism has already become the dominating modality of modern warfare,so it is urgent to study the optimization problem of system-of-systems.For this purpose,it presents a simulation optimization approach for system-of-systems based on orthogonal genetic algorithm and sensitivity analysis.In the proposed approach,it applies the orthogonal genetic algorithm to find some better solutions from the whole feasible space at first;it uses the sensitivity analysis method to obtain the input-output relationship of system-ofsystems by analyzing the evaluated solutions at second;it adopts the sensitivity relations to guide the subsequent heuristic searching of the orthogonal genetic algorithm at third.Many numerical examples were applied to validate the proposed approach.The optimization result suggests that,the proposed approach is feasible,correct and valid.The proposed approach can achieve the optimum solution(or near-optimum solution) of system-of-systems by executing a few times simulation.This approach can be popularized to solve other complex optimization problems.
引文
[1]Adkins M,Kruse J,Younger R.Ubiquitous computing:Omnipresent technology in support of network centric warfare[C]//Proceedings of the 35th Annual Hawaii International Conference on System Sciences.New York,USA:IEEE,2002.551-559.
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[11]罗鹏程,傅攀峰,周经伦.武器装备体系作战能力评估框架[J].系统工程与电子技术,2005,27(1):72-75.Luo P C,Fu P F,Zhou J L.Framework to evaluate the combat capability of weapons SoS[J].System Engineering andElectronics,2005,27(1):72-75.
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[13]蓝海,王雄,王凌.复杂函数全局最优化的改进遗传退火算法[J].清华大学学报(自然科学版),2002,42(9):1237-1240.Lan H,Wang X,Wang L.Improved genetic-annealing algorithm for global optimization of complex functions[J].Journal ofTsinghua University(Science and Technology),2002,42(9):1237-1240.
[14]韩炜,廖振鹏.一种全局优化算法:遗传算法-单纯型法[J].地震工程与工程振动,2001,21(2):6-12.Han W,Liao Z P.A global optimization algorithm:Genetic algorithm-simplex[J].Earthquake Engineering and EngineeringVibration,2001,21(2):6-12.
[15]张毅,杨秀霞.一种新的免疫遗传算法及其在TSP问题中的应用[J].系统工程与电子技术,2005,27(1):117-120.Zhang Y,Yang X X.New immune genetic algorithm and its application on TSP[J].System Engineering and Electronics,2005,27(1):117-120.
[16]Zhong W C,Liu J,Xue M Z,et al.A multiagent genetic algorithm for global numerical optimization[J].IEEE Transactions on Systems,Man,and Cybernetics,Part B:Cybernetics,2004,34(2):1128-1141.
[17]Tsai J T,Liu T K,Chou J H.Hybrid taguchi-genetic algorithm for global numerical optimization[J].IEEE Transactions on Evolutionary Computation,2004,8(4):365-377.
[18]Tu Z,Lu Y.A robust stochastic genetic algorithm(StGA)for global numerical optimization[J].IEEE Transactions onEvolutionary Computation,2004,8(5):456-470.
[2]Maskery M,Krishnamurthy V,Regan C.Game-Theoretic Missile Deflection in Network Centric Warfare[C]//Proceedings of 2005IEEE Networking,Sensing and Control.New York,USA:IEEE,2005,478-483.
[3]王维平,朱一凡,华雪倩.离散事件系统建模与仿真[M].长沙:国防科技大学出版社,1997.Wang W P,Zhu Y F,Hua X J.Modeling and Simulation for Discrete Event System[M].Changsha:National University ofDefense Technology Press,1997.
[4]Jian J,Zhang H M,Guo B,et al.HLA-based Collaborative Simulation Platform for Complex Product Design[C]//Proceedings ofthe 8th International Conference on Computer Supported Cooperative Work in Design.New York,USA:IEEE,2004.462-466.
[5]Xie Y,Tao Y M,Cai W T,et al.Servicing Provisioning for HLA-Based Distributed Simulation on the Grid[C].Proceedings ofWorkshop on Principles of Advanced and Distributed Simulation.New York,USA:IEEE,2005.281-291.
[6]Keane J F,Lutz R R,Myers S E,et al.An architecture for simulation based acquisition[J].Johns Hopkins Apl TechnicalDigest,2000,21(3):348-358.
[7]段红,黄柯棣,李革.基于仿真的采办协同环境研究[J].系统仿真学报,2002,14(2):149-155.Duan H,Huang K D,Li G.Simulation based acquisition collaborative environment[J].Journal of System Simulation,2002,14(2):149-155.
[8]李伯虎,柴旭东,毛媛.现代仿真技术发展中的两个热-点ADS&SBA[J].系统仿真学报,2001,13(1):101-105.Li B H,Chai X D,Mao Y.Two focuses in the development of contemporary simulation technology[J].Journal of SystemSimulation,2001,13(1):101-105.
[9]王凌,张亮,郑大钟.仿真优化研究进展[J].控制与决策,2003,18(3):257-271.Wang L,Zhang L,Zheng D Z.Advances in simulation optimization[J].Control and Decision,2003,18(3):257-271.
[10]Leung Y W,Wang Y P.An orthogonal genetic algorithm with quantization for global numerical optimization[J].IEEE Transactionon Evolutionary Computation,2001,5(1):41-53.
[11]罗鹏程,傅攀峰,周经伦.武器装备体系作战能力评估框架[J].系统工程与电子技术,2005,27(1):72-75.Luo P C,Fu P F,Zhou J L.Framework to evaluate the combat capability of weapons SoS[J].System Engineering andElectronics,2005,27(1):72-75.
[12]Francis M S.The Role of Unmanned Air Vehicles in Advancing System-of-Systems(SoS)Technologies and Capabilities[R].Dayton,Ohio:American Institute of Aeronautics and Astronautics,2003.
[13]蓝海,王雄,王凌.复杂函数全局最优化的改进遗传退火算法[J].清华大学学报(自然科学版),2002,42(9):1237-1240.Lan H,Wang X,Wang L.Improved genetic-annealing algorithm for global optimization of complex functions[J].Journal ofTsinghua University(Science and Technology),2002,42(9):1237-1240.
[14]韩炜,廖振鹏.一种全局优化算法:遗传算法-单纯型法[J].地震工程与工程振动,2001,21(2):6-12.Han W,Liao Z P.A global optimization algorithm:Genetic algorithm-simplex[J].Earthquake Engineering and EngineeringVibration,2001,21(2):6-12.
[15]张毅,杨秀霞.一种新的免疫遗传算法及其在TSP问题中的应用[J].系统工程与电子技术,2005,27(1):117-120.Zhang Y,Yang X X.New immune genetic algorithm and its application on TSP[J].System Engineering and Electronics,2005,27(1):117-120.
[16]Zhong W C,Liu J,Xue M Z,et al.A multiagent genetic algorithm for global numerical optimization[J].IEEE Transactions on Systems,Man,and Cybernetics,Part B:Cybernetics,2004,34(2):1128-1141.
[17]Tsai J T,Liu T K,Chou J H.Hybrid taguchi-genetic algorithm for global numerical optimization[J].IEEE Transactions on Evolutionary Computation,2004,8(4):365-377.
[18]Tu Z,Lu Y.A robust stochastic genetic algorithm(StGA)for global numerical optimization[J].IEEE Transactions onEvolutionary Computation,2004,8(5):456-470.
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