基于多目标进化算法的防空导弹武器目标分配

doi:10.16182/j.issn1004731x.joss.24-0118

系统仿真学报 ›› 2024, Vol. 36 ›› Issue (6): 1298-1308.doi: 10.16182/j.issn1004731x.joss.24-0118

基于多目标进化算法的防空导弹武器目标分配

孙昕¹(), 邢立宁²(), 王锐¹, 王凌³, 石建迈¹, 罗天羽¹

^1.国防科技大学系统工程学院, 湖南长沙 410073
^2.西安电子科技大学电子工程学院, 陕西西安 710126
^3.清华大学自动化系, 北京 100084

收稿日期:2024-01-30 修回日期:2024-04-24 出版日期:2024-06-28 发布日期:2024-06-19
通讯作者: 邢立宁 E-mail:nudtsunx@163.com;lnxing@xidian.edu.cn
第一作者简介:孙昕(1996-)，男，博士生，研究方向为智能优化和资源调度。E-mail：nudtsunx@163.com
基金资助:
国家自然科学基金(62036006);陕西省重点科技创新团队(2023-CX-TD-07);陕西省重点研发计划(2024GH-ZDXM-48)

Air Defense Missile Weapon Target Assignment Based on Multi-objective Evolutionary Algorithm

Sun Xin¹(), Xing Lining²(), Wang Rui¹, Wang Ling³, Shi Jianmai¹, Luo Tianyu¹

^1.College of Systems Engineering, National University of Defense Science and Technology, Changsha 410073, China
^2.College of Electronic Engineering, Xi'an University of Electronic Science and Technology, Xi'an 710071, China
^3.Department of Automation, Tsinghua University, Beijing 100084, China

Received:2024-01-30 Revised:2024-04-24 Online:2024-06-28 Published:2024-06-19
Contact: Xing Lining E-mail:nudtsunx@163.com;lnxing@xidian.edu.cn

摘要/Abstract

摘要：

有效的武器目标分配(weapon-target assignment，WTA)方法对减少作战损失，提高防御效果具有重要意义。针对防空资源分配问题建立合理的数学模型，以最大化目标毁伤效能和最小化雷达资源消耗为优化目标，同时考虑雷达通道数上限等多个约束，在基于分解的多目标进化算法(multi-objective evolutionary algorithm based on decomposition，MOEA/D)基础上进行改进，种群进化过程中自适应调整交叉与变异的概率以提高个体的质量，最终得到一组可供决策者使用的最优解集。实验结果表明：与其他多目标进化算法相比，该算法能得到适应度更高且分布性良好的结果，能够为防空导弹武器目标分配问题提供可行方案。

关键词: 武器目标分配, 多目标进化算法, 自适应参数, 防空导弹

Abstract:

An effective weapon target assignment method can reduce the combat losses and improve the defense effect. A reasonable mathematical model is established for the allocation of air defense resources, aiming at the optimization objectives of maximizing target destruction effectiveness and minimizing radar resource consumption, considering multiple constraints such as the upper limit of radar channels, on the basis of multiobjective evolutionary algorithm based on decomposition (MOEA/D), the probability of crossover and mutation is adaptively adjusted to improve the quality of individuals in the process of population evolution, and a set of optimal solution sets for decision makers is obtained. The results show that, compared with other multi-objective evolutionary algorithms, the algorithm can obtain the higher fitness values and good distributivity, and can provide a feasible solution to the weapon target assignment for air defense missile.

Key words: weapon-target assignment, multi-objective evolutionary algorithm, adaptive parameter, air defence missile

中图分类号:

TP.391.9

孙昕,邢立宁,王锐等 . 基于多目标进化算法的防空导弹武器目标分配[J]. 系统仿真学报, 2024, 36(6): 1298-1308.

Sun Xin,Xing Lining,Wang Rui,et al . Air Defense Missile Weapon Target Assignment Based on Multi-objective Evolutionary Algorithm[J]. Journal of System Simulation, 2024, 36(6): 1298-1308.

图/表 10

图1

图2

图3

图4

表1

算法参数设置

参数	参数值	描述
p	200	种群规模
G	200	最大迭代次数
$M$	24/60	来袭目标个数
$C 0$	$7 / M$	需要交叉的基因概率
$M 0$	$2 / M$	需要变异的基因概率
η	1	概率控制参数

表1

表2

最大目标毁伤效能下不同算法的雷达资源消耗

规模	算法	目标毁伤效能	雷达资源消耗
$M = 24$	GA	57.7	94.70
	NSGA-II	61.7	71.70
	MOEA/D	60.7	78.20
	MOEA/D-AD	62.7	71.05
$M = 60$	GA	113.6	139.80
	NSGA-II	133.1	118.10
	MOEA/D	133.9	127.60
	MOEA/D-AD	143.4	112.60

表2

图5

图6

图7

图8

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基于多目标进化算法的防空导弹武器目标分配

Air Defense Missile Weapon Target Assignment Based on Multi-objective Evolutionary Algorithm

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