[1]马金慧,杨 玉,李存华,等.基于交叉熵-遗传算法的武器目标分配问题研究[J].南京师范大学学报(工程技术版),2022,22(01):068-74.[doi:10.3969/j.issn.1672-1292.2022.01.010]
　Ma Jinhui,Yang Yu,Li Cunhua,et al.Research on Weapon Target Assignment ProblemBased on Cross Entropy-Genetic Algorithm[J].Journal of Nanjing Normal University(Engineering and Technology),2022,22(01):068-74.[doi:10.3969/j.issn.1672-1292.2022.01.010]
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基于交叉熵-遗传算法的武器目标分配问题研究
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南京师范大学学报（工程技术版）[ISSN:1006-6977/CN:61-1281/TN]

卷:
22卷
期数:
2022年01期
页码:
068-74
栏目:
机器学习
出版日期:
2022-03-15

文章信息/Info

Title:
Research on Weapon Target Assignment ProblemBased on Cross Entropy-Genetic Algorithm
文章编号:
1672-1292(2022)01-0068-07
作者:
马金慧; 杨 玉; 李存华; 戴红伟
江苏海洋大学计算机工程学院,江苏 连云港 222005
Author(s):
Ma Jinhui; Yang Yu; Li Cunhua; Dai Hongwei
School of Computer Engineering,Jiangsu Ocean University,Lianyungang 222005,China
关键词:
交叉熵; 交叉熵-遗传算法; 武器目标分配; 最优化问题
Keywords:
cross-entropy; cross-entropy genetic algorithm; weapon target allocation; optimization algorithm problem
分类号:
TP391
DOI:
10.3969/j.issn.1672-1292.2022.01.010
文献标志码:
A
摘要:
武器目标分配问题是军事领域中重要的研究课题,其主要任务是在一定的条件下将武器与来袭目标合理分配,以达到最大的作战收益. 提出了一种将遗传算法融入交叉熵算法的混合算法. 首先,通过交叉熵算法将原本的武器目标分配优化问题与估计问题联系起来,构建满足武器目标分配方案解的离散概率分布矩阵,进而根据矩阵生成代表解的多个样本. 然后,利用遗传算法中的选择、交叉、变异操作增加样本的多样性. 最后,利用推导出最优解的迭代公式来更新矩阵,当满足迭代终止条件时输出的矩阵即为最优解. 分别针对二维单目标函数优化问题和武器目标分配问题进行计算对比,计算结果验证了交叉熵-遗传算法的有效性.
Abstract:
The issue of weapon target assignment(WTA)is an important research topic in the military field. The main task of WTA is to reasonably allocate weapons and incoming targets under certain conditions to achieve the greatest combat gains. This paper proposes a hybrid algorithm that integrates genetic algorithm into cross-entropy algorithm. Firstly,the original WTA optimization problem is connected with the estimation problem through the cross-entropy algorithm. Secondly,the discrete probability distribution matrix that satisfies the solution of the weapon target allocation scheme is constructed. Thirdly,some samples are generated according to the matrix,and then the selection,crossover,and mutation operators of genetic algorithm are used to increase the diversity of the samples. Then,the iterative formula are used to update the matrix. Finally,the matrix is a optimal output when the iteration termination condition is met. In the experimental part,simulation comparisons are carried out for the two-dimensional single-object function optimization problem and WTA problem,experimental results demonstrate the effectiveness of the cross-entropy-genetic algorithm proposed in this paper.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2021-08-31.
基金项目:国家自然科学基金项目(61873105).
通讯作者:戴红伟,博士,教授,研究方向:智能计算与最优化问题. E-mail:hwdai@jou.edu.cn
更新日期/Last Update: 2022-03-15