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KONG L L , WANG J Z , ZHAO P . Solving thedynamic weapon target assignment problem by an improved multi-objective particle swarm optimization algorithm [J ] . Applied Sciences , 2021 , 11 : 9254 . DOI: 10.3390/app11199254 http://doi.org/10.3390/app11199254 https://www.mdpi.com/2076-3417/11/19/9254 https://www.mdpi.com/2076-3417/11/19/9254 Dynamic weapon target assignment (DWTA) is an effective method to solve the multi-stage battlefield fire optimization problem, which can reflect the actual combat scenario better than static weapon target assignment (SWTA). In this paper, a meaningful and effective DWTA model is established, which contains two practical and conflicting objectives, namely, maximizing combat benefits and minimizing weapon costs. Moreover, the model contains limited resource constraints, feasibility constraints and fire transfer constraints. The existence of multi-objective and multi-constraint makes DWTA more complicated. To solve this problem, an improved multiobjective particle swarm optimization algorithm (IMOPSO) is proposed in this paper. Various learning strategies are adopted for the dominated and non-dominated solutions of the algorithm, so that the algorithm can learn and evolve in a targeted manner. In order to solve the problem that the algorithm is easy to fall into local optimum, this paper proposes a search strategy based on simulated binary crossover (SBX) and polynomial mutation (PM), which enables elitist information to be shared among external archive and enhances the exploratory capabilities of IMOPSO. In addition, a dynamic archive maintenance strategy is applied to improve the diversity of non-dominated solutions. Finally, this algorithm is compared with three state-of-the-art multiobjective optimization algorithms, including solving benchmark functions and DWTA model in this article. Experimental results show that IMOPSO has better convergence and distribution than the other three multiobjective optimization algorithms. IMOPSO has obvious advantages in solving multiobjective DWTA problems.

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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.

NAN M Y , ZHU Y F , KANG L , et al . A Modified RL-IGWO algorithm for dynamic weapon-target assignment in frigate defensing UAV swarms [J ] . Electronics , 2022 , 11 : 1796 . DOI: 10.3390/electronics11111796 http://doi.org/10.3390/electronics11111796 https://www.mdpi.com/2079-9292/11/11/1796 https://www.mdpi.com/2079-9292/11/11/1796 Unmanned aerial vehicle (UAV) swarms have significant advantages in terms of cost, number, and intelligence, constituting a serious threat to traditional frigate air defense systems. Ship-borne short-range anti-air weapons undertake terminal defense tasks against UAV swarms. In traditional air defense fire control systems, a dynamic weapon-target assignment (DWTA) is disassembled into several static weapon target assignments (SWTAs), but the relationship between DWTAs and SWTAs is not supported by effective analytical proof. Based on the combat scenario between a frigate and UAV swarms, a model-based reinforcement learning framework was established, and a DWAT problem was disassembled into several static combination optimization (SCO) problems by means of the dynamic programming method. In addition, several variable neighborhood search (VNS) operators and an opposition-based learning (OBL) operator were designed to enhance the global search ability of the original Grey Wolf Optimizer (GWO), thereby solving SCO problems. An improved grey wolf algorithm based on reinforcement learning (RL-IGWO) was established for solving DWTA problems in the defense of frigates against UAV swarms. The experimental results show that RL-IGWO had obvious advantages in both the decision making time and solution quality.

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张进 , 郭浩 , 陈统 . 基于可适应HA的武器-目标分配问题 [J ] . 兵工学报 , 2021 , 42 ( 6 ): 1339 - 1344 . DOI: 10.3969/j.issn.1000-1093.2021.06.025 http://doi.org/10.3969/j.issn.1000-1093.2021.06.025 当前各类智能优化算法求解武器-目标分配问题时，存在耗时长、优化结果不唯一等缺陷，而匈牙利算法具有耗时短、求解结果稳定的优势，但其适应性较差，目前尚未见二者的对比分析文献。针对此现象，对比分析传统匈牙利算法与智能优化算法的耗时性与稳定性，展现了匈牙利算法的优势；提出统一效率矩阵，创建可适用于所有类型目标分配问题的可适应匈牙利算法；通过实例应用验证了可适应匈牙利算法的正确性。

ZHANG J , GUO H , CHEN T . Weapon-targetassignment based on adaptable Hungarian algorithm [J ] . Acta Armamentarii , 2021 , 42 ( 6 ): 1339 - 1344 . (in Chinese) DOI: 10.3969/j.issn.1000-1093.2021.06.025 http://doi.org/10.3969/j.issn.1000-1093.2021.06.025 When various intelligent optimization algorithms are used to solve the weapon-target assignment problem, they have the disadvantages of long time-consuming and non-unique optimization results. Hungary algorithm has the advantages of short time-consuming and stable optimization results, but its adaptability is poor. Currently, the comparison and analysis of intelligent optimization algorithms and Hungarian algorithm has not been reported. For this phenomenon, the time-consuming and stability of traditional Hungarian algorithm and intelligent optimization algorithms are compared, which shows the advantages of Hungarian algorithm. An adaptable Hungarian algorithm that can be applied to all types of weapon-target assignment problems is established by proposing a unified efficiency matrix. And then some examples are used to verify the correctness of the adaptable Hungarian algorithm.

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