基于D-NSGA-GKM算法的多阶段武器协同火力分配方法
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南京理工大学

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TP273

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南京理工大学科研启动费,江苏省自然科学基金(项目号BK20180467)虚拟战场中的智能博弈系统研究


Optimization Method for Multi-stage Collaborative Weapon Firepower Distribution based on D-NSGA-GKM Algorithm
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1.nanjing university of science&technology;2.南京理工大学

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    摘要:

    陆战场防御作战场景中的多阶段武器协同火力分配问题是典型的约束组合优化问题,其目的是为了生成合理有效的火力分配方案。为了更接近实际作战情况,引入了双方的对抗过程,建立了包含敌方作战单元战场剩余价值、作战资源消耗、作战单元战场价值损失的武器火力分配模型。针对多阶段武器协同火力分配问题,在非支配排序遗传算法III(non-dominated sorting genetic algorithm III,NSGA-III)的基础上提出了一种改进的智能算法(D-NSGA-GKM)。首先,引入基于优势度矩阵的非支配排序算法,减少排序过程中的冗余操作,以提高非支配排序效率。然后,在遗传操作阶段引入修复算子,对不可行解进行修复。最后,引入遗传K均值聚类算法对初始参考点进行自动聚类,用聚类质心替代原参考点,在环境选择阶段引入基于惩罚的边界相交距离替代垂直距离,以提高算法的收敛性。实验结果表明,D-NSGA-GKM算法在多阶段武器协同火力分配问题上具有较好的时间性能和收敛性能。

    Abstract:

    The problem of multi-stage weapon collaborative firepower distribution in land battlefield defense is a typical constrained combination optimization problem, which aims to generate a reasonable and effective firepower distribution scheme. In order to get closer to the actual operational, the confrontation game process of both sides has been introduced,and a weapon firepower distribution model including the residual value of enemy combat units, combat resource consumption, and battlefield value loss of combat units is established. An improved intelligent algorithm (D-NSGA-GKM) is proposed based on the non-dominated sorting genetic algorithm III(NSGA-III) for multi-stage collaborative weapon firepower distribution. First, a non-dominated sorting algorithm based on the dominance degree matrix is introduced to reduce redundant operations in the sorting process to improve the efficiency of non-dominated sorting. Then, the repair operator is introduced in the genetic operation stage to repair the infeasible solution. Finally, the genetic K-mean clustering algorithm is introduced to cluster the initial reference points automatically, the centroid of the cluster is used to replace the original reference points, and the penalization-based boundary intersection distance is introduced in the environmental selection stage to replace the vertical distance, to improve the convergence of the algorithm. The experimental results show that the D-NSGA-GKM algorithm has excellent time performance and convergence performance on the problem of multi-stage weapon cooperative fire distribution.

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历史
  • 收稿日期:2020-09-06
  • 最后修改日期:2020-12-19
  • 录用日期:2021-01-08
  • 在线发布日期: 2021-02-04
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