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胡朝晖 , 吕跃 . 反空空导弹弹道及拦截区解算研究 [J ] . 弹道学报 , 2020 , 32 ( 4 ): 1 - 6 . DOI: 10.12115/j.issn.1004-499X(2020)04-001 http://doi.org/10.12115/j.issn.1004-499X(2020)04-001 发射反空空导弹拦截来袭导弹是未来空战时作战飞机的一种重要自卫手段。为研究反空空导弹的拦截性能和计算其有效拦截区,推导了敌方飞机、来袭导弹、自卫飞机和反空空导弹的数学模型,建立了来袭导弹攻击自卫飞机和反空空导弹拦截来袭导弹的空战仿真平台,解算了反空空导弹的拦截区。仿真结果表明,典型空战条件下,反空空导弹对来袭的中远程空空导弹存在有效的拦截区,反空空导弹拦截区的大小与反空空导弹性能、发射前置角、自卫飞机的规避机动、来袭导弹发射距离等因素密切相关,在拦截区内发射反空空导弹,能够极大地提高自卫飞机的战场生存能力。

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于连飞 , 刘进 , 张维明 , 等 . 武器-目标分配问题算法研究综述 [J ] . 数学的实践与认识 , 2016 , 46 ( 2 ): 26 - 32 .

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孔德鹏 , 常天庆 , 郝娜 , 等 . 基于对抗的突击武器与支援武器协同火力打击决策方法 [J ] . 兵工学报 , 2019 , 40 ( 3 ): 629 - 640 . DOI: 10.3969/j.issn.1000-1093.2019.03.023 http://doi.org/10.3969/j.issn.1000-1093.2019.03.023 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。

KONG D P , CHANG T Q , HAO N , et al . Confrontation-based cooperative fire strike decision-making method of assault weapons and support weapons [J ] . Acta Armamentarii , 2019 , 40 ( 3 ): 629 - 640 . (in Chinese) DOI: 10.3969/j.issn.1000-1093.2019.03.023 http://doi.org/10.3969/j.issn.1000-1093.2019.03.023 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.Key

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ZHANG C , ZHANG C . Research on target allocation modeling and optimization algorithm of anti-UAV for antiaircraft artillery [J/OL ] . Journal of Artillery Launch and Control : 1 - 5 [ 2022-02-25 ] . http://kns.cnki.net/kcms/detail/61.1280.TJ.20211231.1030.008.html http://kns.cnki.net/kcms/detail/61.1280.TJ.20211231.1030.008.html http://kns.cnki.net/kcms/detail/61.1280.TJ.20211231.1030.008.html. (in Chinese)

周凌超 . 基于改进模拟退火算法的导弹目标分配方法 [J ] . 工业控制计算机 , 2018 , 31 ( 1 ): 95 - 97 .

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ZHANG H , TAO R , LI Z Y , et al . A feature selection method based on adaptive simulated annealing genetic algorithm [J ] . Acta Armamentarii , 2009 , 30 ( 1 ): 81 - 85 . (in Chinese) feature selection is one of important problems in machine learning and pattern recognition areas. For high demensian data, feature dimension can be decreased under the condition of ensuring data integrity and classification accuracy can be improved by feature selection. A feature selection method based on adaptive simulated annealing genetic algorithm was proposed, which embeds the sim?ulated annealing algorithm in the circle of adaptive genetic algorithm and uses the feature that simulat?ed annealing algorithm has the strong ability of local search and makes searching process avoid sinking into the local optimal solution, to solve the shortcomings of slow convergence speed and high time com?plexity. The experiment results show that the method can guarantee the correct rate of classification and improve the efficiency of feature selection.

朱建文 , 赵长见 , 李小平 , 等 . 基于强化学习的集群多目标分配与智能决策方法 [J ] . 兵工学报 , 2021 , 42 ( 9 ): 2040 - 2048 .

ZHU J W , ZHAO C J , LI X P , et al . Multi-target assignment and intelligent decision based on reinforcement learning [J ] . Acta Armamentarii , 2021 , 42 ( 9 ): 2040 - 2048 . (in Chinese) DOI: 10.3969/j.issn.1000-1093.2021.09.025 http://doi.org/10.3969/j.issn.1000-1093.2021.09.025 A reinforcement learning-based swarm intelligent decision-making method of cooperative multi-target attack under high-dynamic situation is proposed. The composite evaluation criteria of attack performance is established, including the evaluation of attack superiority based on relative motion information and the threat evaluation based on the inherent information of target. To evaluate the attack-defence effectiveness, a cost-effectiveness ratio index is designed by combining attack performance, penetration probability and attack cost together. In addition, a multi-target decision-making architecture based on reinforcement learning is constructed, and an action space with allocation vectors as basic elements and a state space based on quantified performance indicators are designed. Q-Learning is employed to make intelligent decisions on cooperative attack plans, including missile selection and target assignment. The simulated results show that reinforcement learning can achieve multi-target online decision-making with the optimal offensive and defensive effectiveness, and its computational efficiency has more obvious advantages than that of particle swarm optimizer.

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