基于改进人工蜂群算法的地面作战武器-目标分配

doi:10.12382/bgxb.2022.0294

摘要/Abstract

摘要：

为了提高地面分队火力分配的科学性,构建基于打击效益的地面作战武器目标分配(WTA)模型,针对战场上打击的收益和代价,制定合理的优化函数。通过对目标的威胁评估,判定打击的迫切性和必要性;通过对目标的战场价值判断,计算毁伤价值收益;通过目标对武器的毁伤概率,预估我方武器的损失;通过分析敌我兵力对比和弹药储备情况,量化弹药的价值;通过分析敌我双方的战术意图,衡量打击的必要性和战术意图暴露的代价。针对WTA模型求解问题,提出一种改进人工蜂群算法,以提高算法的搜索方向性和迭代后期跳出局部最优能力,同时采用基于武器目标组合库的种群初始化策略,提高了算法初期种群质量。仿真算例表明了所提模型的科学性以及新算法在收敛速度、收敛精度和鲁棒性方面的优势。

关键词: 武器目标分配, 打击效益, 人工蜂群算法, 武器目标组合

Abstract:

This paper presents a ground combat Weapon Target Assignment (WTA) model that enhances the validity of firepower allocation for ground units. The model incorporates shooting effectiveness as a key factor and formulates an optimization function considering the benefits and costs of attack decisions on the battlefield. By assessing the threat level of targets, the model determines the urgency and necessity of an attack. By evaluating the battlefield value of targets, it calculates the threat reduction value. The model predicts enemy attack plans and estimates weapon losses based on the probability of damage inflicted by targets. It quantifies the value of ammunition by comparing forces with ammunition reserves. By analyzing the tactical intentions of both sides, the necessity of a strike is weighed against the cost of tactical intent exposure. To solve that WTA model, an improved artificial bee colony algorithm is proposed. This algorithm improves the search directionality of the algorithm and the ability to escape local optimum at the end of each iteration. At the same time, a population initialization strategy based on the Weapon Target Combination Library is adopted to improve the initial population quality of the algorithm. Simulation examples show the soundness of the proposed model and the advantages of the improved algorithm in terms of convergence speed, convergence accuracy, and robustness.

Key words: weapon target assignment, shooting effectiveness, artificial bee colony algorithm, weapon-target pairs

褚凯轩, 常天庆, 张雷. 基于改进人工蜂群算法的地面作战武器-目标分配[J]. 兵工学报, 2023, 44(7): 2171-2183.

CHU Kaixuan, CHANG Tianqing, ZHANG Lei. A Ground Combat Weapon Target Assignment Model Based on Shooting Effectiveness and Improved Artificial Bee Colony Algorithm[J]. Acta Armamentarii, 2023, 44(7): 2171-2183.

图/表 10

参考文献 24

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战例	p	q	Th	Vt	Vw	b	ε	效益打击/等待	决策
1	0.76	0.18	4.45	5	5	8	0.25	4.41/-0.90	打击
2	0.76	0.56	4.20	6	8	5	0.40	-0.93/-4.48	打击
3	0.70	0.25	0.35	7	3	2	0.25	1.15/-0.75	打击
4	0.41	0.18	2.15	3	2	10	0.40	0.45/-0.36	打击
5	0.04	0.08	1.10	3	1	1	0.15	-5.07/-0.08	等待
6	0.42	0.60	0.55	10	6	10	0.90	-5.07/-3.6	等待
7	0.44	0.25	4.00	3	5	10	0.40	-0.67/-1.25	打击

战例	p	q	Th	Vt	Vw	b	ε	效益打击/等待	决策
1	0.76	0.18	4.45	5	5	8	0.25	4.41/-0.90	打击
2	0.76	0.56	4.20	6	8	5	0.40	-0.93/-4.48	打击
3	0.70	0.25	0.35	7	3	2	0.25	1.15/-0.75	打击
4	0.41	0.18	2.15	3	2	10	0.40	0.45/-0.36	打击
5	0.04	0.08	1.10	3	1	1	0.15	-5.07/-0.08	等待
6	0.42	0.60	0.55	10	6	10	0.90	-5.07/-3.6	等待
7	0.44	0.25	4.00	3	5	10	0.40	-0.67/-1.25	打击

WTA模型	设置条件	打击方案	毁伤概率
文献[7]模型	K=0.7,0.7,0.7,0.7	[1,3,4,0,2,4,0,4]	[0.7600,0.7400,0.7300,0.7178]
	K=0.8,0.8,0.8,0.8	[2,3,2,4,1,3,1,4]	[0.8173,0.8266,0.8056,0.7920]
	K=0.9,0.9,0.9,0.9	[1,3,1,4,2,2,3,4]	[0.8776,0.8934,0.9055,0.7920]
文献[9]模型	[ξ,ε₁,ε₂,σ₁,σ₂]=[0.7,0.02,0.02,0.2,0.67]	[1,3,4,0,2,4,0,4]	[0.7600,0.7400,0.7300,0.7178]
	[ξ,ε₁,ε₂,σ₁,σ₂]=[0.8,0.02,0.02,0.2,0.67]	[2,3,2,4,1,3,1,4]	[0.8173,0.8266,0.8056,0.7920]
	[ξ,ε₁,ε₂,σ₁,σ₂]=[0.9,0.02,0.02,0.2,0.67]	[1,3,1,4,2,2,3,4]	[0.8776,0.8934,0.9055,0.7920]
文献[11]模型	D=0.7,0.7,0.7,0.7	[1,3,0,4,2,0,0,4]	[0.7600,0.7400,0.7300,0.7920]
文献[11]模型	D=0.8,0.8,0.8,0.8	无解
标准WTA模型		[1,3,2,4,2,1,3,4]	[0.9064,0.8674,0.9055,0.7920]
本文模型		[1,3,2,4,2,1,0,4]	[0.9064,0.8674,0.7300,0.7920]

WTA模型	设置条件	打击方案	毁伤概率
文献[7]模型	K=0.7,0.7,0.7,0.7	[1,3,4,0,2,4,0,4]	[0.7600,0.7400,0.7300,0.7178]
	K=0.8,0.8,0.8,0.8	[2,3,2,4,1,3,1,4]	[0.8173,0.8266,0.8056,0.7920]
	K=0.9,0.9,0.9,0.9	[1,3,1,4,2,2,3,4]	[0.8776,0.8934,0.9055,0.7920]
文献[9]模型	[ξ,ε₁,ε₂,σ₁,σ₂]=[0.7,0.02,0.02,0.2,0.67]	[1,3,4,0,2,4,0,4]	[0.7600,0.7400,0.7300,0.7178]
	[ξ,ε₁,ε₂,σ₁,σ₂]=[0.8,0.02,0.02,0.2,0.67]	[2,3,2,4,1,3,1,4]	[0.8173,0.8266,0.8056,0.7920]
	[ξ,ε₁,ε₂,σ₁,σ₂]=[0.9,0.02,0.02,0.2,0.67]	[1,3,1,4,2,2,3,4]	[0.8776,0.8934,0.9055,0.7920]
文献[11]模型	D=0.7,0.7,0.7,0.7	[1,3,0,4,2,0,0,4]	[0.7600,0.7400,0.7300,0.7920]
文献[11]模型	D=0.8,0.8,0.8,0.8	无解
标准WTA模型		[1,3,2,4,2,1,3,4]	[0.9064,0.8674,0.9055,0.7920]
本文模型		[1,3,2,4,2,1,0,4]	[0.9064,0.8674,0.7300,0.7920]

小规模		中规模		大规模
m>n	m<n	m>n	m<n	m>n	m<n
5W-3T	3W-5T	20W-10T	10W-20T	60W-30T	30W-60T
8W-4T	4W-8T	30W-15T	15W-30T	100W-50T	50W-100T