A Ground Combat Weapon Target Assignment Model Based on Shooting Effectiveness and Improved Artificial Bee Colony Algorithm

doi:10.12382/bgxb.2022.0294

Abstract

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

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.

Figures/Tables 10

References 24

[1]	KLINE A, AHNER D, HILL R. The weapon-target assignment problem[J]. Computers & Operations Research, 2019, 105: 226-236. doi: 10.1016/j.cor.2018.10.015 URL
[2]	孔德鹏, 常天庆, 郝娜, 等. 基于对抗的突击武器与支援武器协同火力打击决策方法[J]. 兵工学报, 2019, 40(3): 629-640. doi: 10.3969/j.issn.1000-1093.2019.03.023
	KONG D P, CHANG T Q, HAO N, et al. Confrontation-based cooperative fire strike decision-making method of assault weapons and support weapons[J]. Acta Armamentarii, 2019, 40(3): 629-640. (in Chinese) doi: 10.3969/j.issn.1000-1093.2019.03.023
[3]	ANDERSEN A C, PAVLIKOV K, TOFFOLO T A M. Weapon-target assignment problem: exact and approximate solution algorithms[J]. Annals of Operations Research, 2022, 312:581-606. doi: 10.1007/s10479-022-04525-6
[4]	CAO M, FANG W G. Swarm intelligence algorithms for weapon-target assignment in a multilayer defense scenario: a comparative study[J]. Symmetry, 2020, 12(5): 1-20. doi: 10.3390/sym12010001 URL
[5]	HOCAOĞLU M F. Weapon target assignment optimization for land based multi-air defense systems: a goal programming approach[J]. Computers & Industrial Engineering, 2019, 128: 681-689. doi: 10.1016/j.cie.2019.01.015 URL
[6]	BOGDANOWICZ Z R. Advanced input generating algorithm for effect-Based weapon-target pairing optimization[J]. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 2012, 42(1): 276-280. doi: 10.1109/TSMCA.2011.2159591 URL
[7]	BOGDANOWICZ Z R, TOLANO A, PATEL K, et al. Optimization of weapon-target pairings based on kill probabilities[J]. IEEE Transactions on Cybernetics, 2013, 43(6):1835-1844. doi: 10.1109/TSMCB.2012.2231673 pmid: 24273148
[8]	BOGDANOWICZ Z R, PATEL K. Quick collateral damage estimation based on weapons assigned to targets[J]. IEEE Transactions on Systems Man & Cybernetics Systems, 2015, 45(5):762-769.
[9]	陈军伟, 常天庆, 张雷, 等. 面向装甲分队战法运用的两阶段WTA模型[J]. 系统工程与电子技术, 2016, 38(6): 1326-1331.
	CHEN J W, CHANG T Q, ZHANG L, et al. Two-stage model of WTA oriented armored unit combat method[J]. Systems Engineering and Electronics, 2016, 38(6): 1326-1331. (in Chinese) doi: 10.3969/j.issn.1001-506X.2016.06.17
[10]	白帆, 常天庆, 王钦钊. 基于模糊火力适度原则的坦克分队WTA模型研究[J]. 系统仿真学报, 2012, 24(6):1161-1164.
	BAI F, CHANG T Q, WANG Q Z. Research on fuzzy moderate firing principle-based WTA model for tank unit[J]. Journal of System Simulation, 2012, 24(6):1161-1164. (in Chinese)
[11]	石章松, 吴鹏飞, 刘志超. 基于最小资源损耗的武器目标动态分配[J]. 海军工程大学学报, 2019, 31(4):64-71.
	SHI Z S, WU P F, LIU Z C. Dynamic weapon-targets assignment based on minimum resource depletion[J]. Journal of Naval University of Engineering, 2019, 31(4): 64-71. (in Chinese)
[12]	GAO C Q, KOU Y X, LI Y, et al. Multi-objective weapon target assignment based on D-NSGA-Ⅲ-A[J]. IEEE Access, 2019: 50240-50254.
[13]	LI J, CHEN J, XIN B, et al. Efficient multi-objective evolutionary algorithms for solving the multi-stage weapon target assignment problem:a comparison study[C]//Proceedings of 2017 IEEE Congress on Evolutionary Computation.San Sebastian, Spain, IEEE, 2017: 435-442.
[14]	LI Y, KOU Y X, LI Z W, et al. A modified pareto ant colony optimization approach to solve biobjective weapon-target assignment problem[J]. International Journal of Aerospace Engineering, 2017(8):1-14.
[15]	LLOYD S P, WITSENHAUSEN H S. Weapons allocation is NP-complete[C]//Proceedings of 1986 Summer Computer Simulation Conference. San Diego, CA, US: The Society, 1986: 1054-1058.
[16]	GUO D, LIANG Z X, JIANG P, et al. Weapon-target assignment for multi-to-multi interception with grouping constraint[J]. IEEE Access, 2019: 34838-34849.
[17]	LIU X, LIANG J, LIU D Y, et al. Weapon-target assignment in unreliable peer-to-peer architecture based on adapted artificial bee colony algorithm[J]. Frontiers of Computer Science, 2022, 16(1): 1-9.
[18]	GUO D, DONG X W, LI Q D, et al. Weapon target assignment method with grouping constraints for interception based on artificial bee colony algorithm[C]// Proceedings of the 2019 IEEE 15th International Conference on Control and Automation.Washington,D.C., US:IEEE, 2019: 1385-1390.
[19]	CHANG T Q, KONG D P, HAO N, et al. Solving the dynamic weapon target assignment problem by an improved artificial bee colony algorithm with heuristic factor initialization[J]. Applied Soft Computing, 2018, 70:845-863. doi: 10.1016/j.asoc.2018.06.014 URL
[20]	HUANG J, LI B C, ZHAO Y J. Target threat assessment based on intuitionistic fuzzy sets choquet integral[J]. Applied Mechanics and Materials, 2013, 433-435: 736-743.
[21]	KARABOGA D. An idea based on honey bee swarm for numerical optimization[R]. Kayseri, Türkiye: Erciyes University, 2005.
[22]	LIN Q Z, ZHU M M, LI Z G, et al. A novel artificial bee colony algorithm with local and global information interaction[J]. Applied Soft Computing, 2018, 62: 702-735. doi: 10.1016/j.asoc.2017.11.012 URL
[23]	KARABOGA D, GORKEMLI B. A quick artificial bee colony(qABC) algorithm and its performance on optimization problems[J]. Applied Soft Computing, 2014, 23: 227-238. doi: 10.1016/j.asoc.2014.06.035 URL
[24]	CUI L Z, LI G H, LIN Q Z, et al. A novel artificial bee colony algorithm with depth-first search framework and elite-guided search equation[J]. Information Sciences, 2016, 367: 1012-1044.

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