基于对抗的突击武器与支援武器协同火力打击决策方法
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摘要
为满足多类型武器协同火力优化打击的需求,提出了一种基于对抗的突击武器与支援武器协同火力打击决策方法。以突击武器"点对点"打击和远程火力支援武器"面杀伤"的协同为研究对象,考虑具有对抗特性的火力打击决策优化过程,以突击武器对目标的打击决策、目标对突击武器的打击决策以及支援武器的炮弹落点位置为优化变量,建立了以对抗双方剩余价值比值为目标函数的协同火力打击决策优化模型。提出了基于人工蜂群算法双层迭代优化的协同火力打击决策优化模型求解方法。目标分配决策变量采用整数编码,利用罚函数方法处理约束条件,将决策模型转化为无约束混合整数优化问题;针对算法实现过程,分析了双层迭代人工蜂群求解算法的计算复杂度。通过一个协同火力打击算例验证了协同火力打击决策模型和求解算法的合理性和有效性。
A decision-making method for the cooperative fire strike( CFS) of assault weapons and support weapons in confrontation is proposed. And a decision-making model for CFS is established based on the ratio of friend or foe's residual values by studying the "point to point"strike of assault weapons and the"area damage"of long-range firepower support weapons,and the optimization process of decision-making of fire strike is considered. The decision of the assault weapons attacking the targets,the decision of the targets attacking the assault weapons and the drop points of projectiles launched from supporting weapons are taken as the optimization variables in decision-making model. A two-level iterative optimization method based on artificial bee colony( ABC) algorithm is proposed to solve the CFS decision-making optimization model. The integer is used to encode the decision variables,and the penalty function method is used to deal with the constraints. The decision-making model is transformed into an unconstrained mixed integer optimization problem. In view of the implementation process of the proposed algorithm,the computational complexity of the two-level iterative ABC algorithm is analyzed. A CFS example is used to verify the rationality and effectiveness of the collaborative fire strike decision-making model and the solving algorithm.
A decision-making method for the cooperative fire strike( CFS) of assault weapons and support weapons in confrontation is proposed. And a decision-making model for CFS is established based on the ratio of friend or foe's residual values by studying the "point to point"strike of assault weapons and the"area damage"of long-range firepower support weapons,and the optimization process of decision-making of fire strike is considered. The decision of the assault weapons attacking the targets,the decision of the targets attacking the assault weapons and the drop points of projectiles launched from supporting weapons are taken as the optimization variables in decision-making model. A two-level iterative optimization method based on artificial bee colony( ABC) algorithm is proposed to solve the CFS decision-making optimization model. The integer is used to encode the decision variables,and the penalty function method is used to deal with the constraints. The decision-making model is transformed into an unconstrained mixed integer optimization problem. In view of the implementation process of the proposed algorithm,the computational complexity of the two-level iterative ABC algorithm is analyzed. A CFS example is used to verify the rationality and effectiveness of the collaborative fire strike decision-making model and the solving algorithm.
引文
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[19]徐兰,苏翔.求解双层规划优化问题的层次风驱动优化算法[J].控制与决策,2016,31(10):1894-1898.XU L,SU X. Hierarchical wind driven optimization method for solving bi-level programming problem[J]. Control and Decision,2016,31(10):1894-1898.(in Chinese)
[20] CUI L Z,LI G H,LIN Q Z,et al. A novel artificial bee colony algorithm with depth-first search framework and elite-guided search equation[J]. Information Sciences,2016,367/368(11):1012-1044.
[2]常天庆,陈军伟,郝娜,等.装甲分队动态武器目标分配中蚁群算法终止控制[J].系统工程与电子技术,2015,37(2):343-347.CHANG T Q,CHEN J W,HAO N,et al. Terminating control of ant colony algorithm for armored unit dynamic weapon-target assignment problem[J]. Systems Engineering and Electronics,2015,37(2):343-347.(in Chinese)
[3]李臣明,宦超,石怀龙.某箱式火箭炮对面目标分布式杀伤最优火力分配[J].兵工学报,2017,38(9):1699-1704.LI C M,HUAN C,SHI H L. Optimal fire distribution of a rocket launcher against area target[J]. Acta Armamentarii,2017,38(9):1699-1704.(in Chinese)
[4] HOSEIN P A,ATHAN M S. Preferential defense strategies. Part I:the static case:LIPS-P-2002[R]. Cambridge,MA,US:Massachusetts Institute of Technology,1990.
[5] HOSEIN P A,ATHAN M S. Preferential defense strategies. Part II:the dynamic case:LIPS-P-2003[R]. Cambridge,MA,US:Massachusetts Institute of Technology,1990.
[6] SHALUMOV V,SHIMA T. Weapon-target-allocation strategies in multiagent target-missile-defender engagement[J]. Journal of Guidance,Control,and Dynamics,2017,40(10):2452-2464.
[7] XIN B,WANG Y P,CHEN J. An efficient marginal-return-based constructive heuristic to solve the sensor-weapon-target assignment problem[J]. IEEE Transactions on Systems,Man,and Cybernetics:Systems,2018,99:1-12.
[8]陈军伟,常天庆,张雷,等.面向装甲分队战法运用的两阶段WTA模型[J].系统工程与电子技术,2016,38(6):1326-1331.CHEN J W,CHANG T Q,ZHANG L,et al. Two-stage model of WTA oriented armored unit combat method[J]. Systems Engineering and Electronics,2016,38(6):1326-1331.(in Chinese)
[9] LLOYD S P,WITSENHAUSEN H S. Weapons allocation is NPcomplete[C]∥Proceedings of IEEE Summer Simulation Conference. Reno,NV,US:IEEE,1986:1054-1058.
[10] LEE M Z. Constrained weapon-target assignment:enhanced very large scale neighborhood search algorithm[J]. IEEE Transactions on Systems,Man,and Cybernetics-Part A:Systems and Humans,2010,40(1):198-204.
[11] WANG Y,JIN L,HUANG W L,et al. Dynamic weapon target assignment based on intuitionistic fuzzy entropy of discrete particle swarm[J]. China Communications 2017,14(1):169-179.
[12]张春美,陈杰,辛斌.武器目标分配问题的离散差分进化算法[J].北京理工大学学报,2014,34(3):289-293.ZHANG C M,CHEN J,XIN B. A discrete differential evolution algorithm for the weapon target assignment problem[J]. Transactions of Beijing Institute of Technology,2014,34(3):289-293.(in Chinese)
[13] REZENDE M D,LIMA B S L P D,GUIMARES S. A greedy ant colony system for defensive resource assignment problems[J]. Applied Artificial Intelligence,2018,32(2):1-15.
[14] GALATI D G,SIMAAN M A. Effectiveness of the Nash strategies in competitive multi-team target assignment problems[J].IEEE Transactions on Aerospace&Electronic Systems,2007,43(1):126-134.
[15] LEBOUCHER C,SHIN H S,LE MENEC S,et al. Novel evolutionary game based multi-objective optimisation for dynamic weapon target assignment[C]∥Proceedings of the 19th World Congress. Cape Town,South Africa:the International Federation of Automatic Control,2014:3936-3941.
[16] YAO Z X,LI M,CHEN Z J,et al. Mission decision-making method of multi-aircraft cooperatively attacking multi-target based on game theoretic framework[J]. Chinese Journal of Aeronautics,2016,29(6):1685-1694.
[17] ANGULURI R,NANDAR L,SWAGATAM D,et al. Computing with the collective intelligence of honey bees-a survey[J].Swarm and Evolutionary Computation,2017,32(2):25-48.
[18] DERVIS K,BAHRIYE B. A powerful and efficient algorithm for numerical function optimization:artificial bee colony(ABC)algorithm[J]. Journal of Global Optimization,2007,39(3):459-471.
[19]徐兰,苏翔.求解双层规划优化问题的层次风驱动优化算法[J].控制与决策,2016,31(10):1894-1898.XU L,SU X. Hierarchical wind driven optimization method for solving bi-level programming problem[J]. Control and Decision,2016,31(10):1894-1898.(in Chinese)
[20] CUI L Z,LI G H,LIN Q Z,et al. A novel artificial bee colony algorithm with depth-first search framework and elite-guided search equation[J]. Information Sciences,2016,367/368(11):1012-1044.