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LIU Q , LU Y J , CHEN K B , et al . Combat task decomposition EVA method based on binary constraints of task subject [J ] . Systems Engineering and Electronics , 2022 , 44 ( 7 ): 2201 - 2210 . (in Chinese) DOI: 10.12305/j.issn.1001-506X.2022.07.16 http://doi.org/10.12305/j.issn.1001-506X.2022.07.16 Aiming at the arbitrariness and uncertainty in the decomposition of complex combat tasks, a extraction-verification-adjustment (EVA) task decomposition method is proposed, which is formed by the extraction, verification, and adjustment of the sub task set, considering the binary constraints such as the capability attributes and structural characteristics of the task subject. First, the global task space is constructed, and the subtask set extraction method based on task matching is proposed. Secondly, according to the binary constraints of the task subject's ability attributes and structural characteristics, a quantitative adjustment model of subtask sets is established. By improving the elitist retention strategy and introducing task decomposition granularity and the dynamic adjustment strategy of crossover mutation probability, the improved non-dominated sorting genetic algorithm-Ⅱ (INSGA-Ⅱ) algorithm is proposed. Finally, the simulation results verify that INSGA-Ⅱ has the advantages of diversity, convergence in solution set and timeliness performance compared with the traditional optimization algorithms. The results show that the method proposed in this paper enable decision makers to adjust and control the task decomposition results according to the actual situation of the task subject, and overcome the problem that the traditional methods rely on subjective experience and ignore the constraints of the ability attributes and structural characteristics of the task subject.

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杜伟伟 , 陈小伟 . 作战任务层次化分解方法 [J ] . 兵工学报 , 2021 , 42 ( 12 ): 2771 - 2782 . DOI: 10.3969/j.issn.1000-1093.2021.12.025 http://doi.org/10.3969/j.issn.1000-1093.2021.12.025 根据作战任务规划对任务分解辅助决策的迫切需求，提出一种基于效用筛选的作战任务多方案分解和优选框架。针对任务描述通用性差等不足，基于5W理论对作战任务进行规范化描述，实现对各类作战任务的通用化定量描述；针对任务分解影响因素和约束条件的复杂性，从任务场景、领域知识两个方面建立任务分解模型，将任务分解转化为多约束运筹优化问题；针对传统任务分解方法单一、分解方案优选手段缺乏等问题，设计一种基于效用筛选的层次任务分解方法，实现多套分解方案生成和筛选优化。以具体作战任务为例，对该方法进行验证。仿真实验结果表明，层次任务分解方法能够对生成的多套方案进行优选，最优概率达到90%以上，满足实际作战辅助决策需求。

DU W W , CHEN X W . Operational task hierarchical decomposition [J ] . Acta Armamentarii , 2021 , 42 ( 12 ): 2771 - 2782 . (in Chinese) DOI: 10.3969/j.issn.1000-1093.2021.12.025 http://doi.org/10.3969/j.issn.1000-1093.2021.12.025 Operational task decomposition is a key step for the military operation planning in computer-aided decision-making. To this end，a multi-scheme task decomposition and selection framework is presented. A standard task description method is proposed to realize the quantitative operational task description based on 5W theory. A task decomposition model is established according to task scenario and domain knowledge，which formulates the task decomposition as a multi-constraint optimization problem and alleviates the problem complexity caused by various influential factors and constraints. A hierarchical task decomposition method is presented to generate the multiple solutions and further finalize an optimized solution，which overcomes the unavailability of sufficient task decomposition and optimization means.The proposed method is validated based on a specific battle task decomposition，and the simulated results demonstrate the effectiveness and practicability of the proposed method，achieving over 90% optimum rate.

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沈丙振 , 缪建明 , 李晓菲 , 等 . 基于改进结构方程模型的陆军武器装备体系作战能力评估模型 [J ] . 兵工学报 , 2021 , 42 ( 11 ): 2503 - 2512 . DOI: 10.3969/j.issn.1000-1093.2021.11.023 http://doi.org/10.3969/j.issn.1000-1093.2021.11.023 作战能力评估对陆军武器装备体系发展论证和作战运用具有重要意义，其评估模型的构建又是其中最为关键的一项内容。分析基于改进结构方程模型（SEM）的陆军武器装备体系作战能力评估步骤，构建陆军武器装备体系作战能力评估指标体系，并对现有SEM进行改进。通过线性加权和法对多维度体系作战能力评估指标体系进行降维处理，提高SEM的计算效率，实现陆军武器装备体系作战能力的快速计算与综合评估。改进SEM中的聚合指标权重求解分别采用熵权法和层次分析法，将2种权重聚合指标值参与到改进SEM中计算的体系作战能力评估结果，与直接用SEM的体系作战能力评估结果进行对比。仿真实验结果表明，体系作战能力评估结果的变化趋势基本一致，实现了对改进SEM可信度和时效性的验证分析。

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马也 , 范文慧 , 常天庆 . 基于智能算法的无人集群防御作战方案优化方法 [J ] . 兵工学报 , 2022 , 43 ( 6 ): 1415 - 1425 . DOI: 10.12382/bgxb.2021.0339 http://doi.org/10.12382/bgxb.2021.0339 兵力部署与任务分配是无人集群防御作战的重要过程，有效利用集群中有限的兵力并发挥出最高的作战效能对提高无人集群的作战胜率至关重要，进行高效的作战任务分配能够协调集群一致性并更好完成作战任务。针对无人集群防御作战中的关键作战方案，研究无人集群防御作战的兵力部署及协同任务分配优化问题。构建基于智能体技术的无人集群防御作战模型，量化无人集群兵力部署所需的关键参数，对作战区域与兵力进行规划，设计目标函数。提出一种自适应遗传算法，解决无人集群的兵力部署问题。算法可根据实时运行情况动态调整目标函数、交叉率和变异率，保证适应度值较高个体的传承并避免算法出现局部最优。进行防御作战仿真，为验证无人集群兵力部署的效果，提出一种基于深度Q网络的深度强化学习改进算法，解决无人集群任务分配问题，对部署好的无人集群进行任务分配并作战。该算法能够自适应调整Q值，避免算法因过度估计造成无法收敛至最优解。防御作战实验结果表明，所提出的无人集群兵力部署及协同任务分配方法可有效提高防御作战的成功率，实现无人集群的自主协同及智能对抗。

MA Y , FAN W H , CHANG T Q . Optimization method of unmanned swarm defensive combat scheme based on intelligent algorithm [J ] . Acta Armamentarii , 2022 , 43 ( 6 ): 1415 - 1425 . (in Chinese) DOI: 10.12382/bgxb.2021.0339 http://doi.org/10.12382/bgxb.2021.0339 Troop deployment and task allocation are important processes of unmanned swarm defense operations, it's very important to make effective use of the limited forces in the swarm and wield the highest operational efficiency to improve the battle victory rate of unmanned swarm. At the same time, efficient combat task allocation can coordinate the consistency of swarm and better complete combat tasks. Aiming at the key combat plan in unmanned swarm defense operations, the optimization of troop deployment and coordinated task allocation in unmanned swarm defense operations is studied. A multi-agent-based unmanned swarm defensive combat model is built to quantify the key parameters required for the deployment of unmanned swarm forces, the model plans the combat area and troops, and designs the objective function. An adaptive genetic algorithm is proposed to solve the deployment problem of unmanned swarms. The proposed algorithm could dynamically adjust the objective function，crossover rate and mutation rate according to the real-time operating conditions，ensuring the inheritance of individuals with higher fitness values and avoiding the local optimization of the algorithm. A defensive operation is simulated to verify the deployment effectiveness of unmanned swarm forces. The improved deep reinforcement learning algorithm based on deep Q network is proposed to find a solution to the task allocationfor the deployed unmanned swarms. The proposed algorithm could adjust the Q value through self-adaption to avoid non-convergence caused by the algorithm's overestimation and find the optimal solution.The experimental results of defensive operations show that the proposed unmanned swarm force deployment and coordinated task allocation method could effectively improve the success rate of defensive operations，and realize the autonomous coordination and intelligent confrontation of unmanned swarms.

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