基于蜂群算法的坦克阵地部署与火力分配模型

doi:10.12305/j.issn.1001-506X.2022.02.24

摘要/Abstract

摘要：

为了解决坦克分队进攻战斗中的兵力部署和火力协同问题, 提出坦克阵地部署模型和坦克火力分配模型, 前者解决坦克分队从集结区域到作战区域的兵力分配问题, 后者解决坦克接敌后的火力协同问题。针对坦克作战中的对抗特性, 建立确定型火力对抗模型, 在火力分配模型中体现敌我动态对抗过程。为了求取坦克阵地部署和火力分配最优方案, 采取双层迭代策略, 底层迭代求解火力分配模型, 上层迭代调用底层迭代的结果, 寻优坦克阵地部署最优方案。算法方面, 基于模型的具体特点和先验知识, 对人工蜂群(artifitial bee colony, ABC)算法进行有针对性的设计, 提高了算法的收敛速度和收敛精度。仿真实验说明了本文对人工蜂群算法设计的有效性和双层迭代寻优策略的合理性。

关键词: 坦克分队, 阵地部署, 火力分配, 人工蜂群算法

Abstract:

In order to solve the problem of troop deployment and firepower coordination of tank detachment in offensive combat, this paper puts forward the model of tank position deployment and the model of tank firepower allocation. The former solves the problem of troop allocation from assembly area to combat area of tank detachment, and the latter solves the problem of firepower coordination of tank detachment after contacting enemies. According to the antagonistic characteristics of tank operations, a deterministic firepower confrontation model is established, and the dynamic antagonistic process is embodied in the firepower distribution model. In order to find the optimal plan of tank position deployment and firepower allocation, the two-layer iteration strategy is adopted, the bottom iteration is used to solve the firepower allocation model, and the upper iteration is used to find the optimal plan of tank position deployment by calling the results of the bottom iteration. Based on the specific characteristics and prior knowledge of the model, the artificial bee colony (ABC) algorithm is designed to improve the convergence speed and accuracy of the algorithm. The simulation results show the effectiveness of the ABC algorithm and the rationality of the two-layer iterative optimization strategy.

Key words: tank detachment, troop deployment, firepower allocation, artificial bee colony (ABC) algorithm

中图分类号:

E920.8

褚凯轩, 常天庆, 孔德鹏, 张雷, 孙皓泽. 基于蜂群算法的坦克阵地部署与火力分配模型[J]. 系统工程与电子技术, 2022, 44(2): 546-556.

Kaixuan CHU, Tianqing CHANG, Depeng KONG, Lei ZHANG, Haoze SUN. Bee colony algorithm based model of tank troop deployment and firepower allocation[J]. Systems Engineering and Electronics, 2022, 44(2): 546-556.

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小规模			中规模			大规模
m	a	n	m	a	n	m	a	n
10	4	3	20	7	7	40	10	15
Y₁=[2, 3, 2, 3] Y₂=[3, 1, 4, 2] Y₃=[1, 3, 3, 3]			Y₁=[3, 2, 3, 3, 3, 3, 3] Y₂=[4, 1, 3, 5, 2, 2, 3] Y₃=[3, 5, 3, 3, 1, 1, 4]			Y₁=[3, 5, 3, 6, 5, 4, 6, 2, 3, 3] Y₂=[3, 5, 3, 5, 1, 7, 1, 1, 9, 5] Y₃=[6, 3, 4, 3, 4, 5, 1, 3, 4, 7]

射击区域	目标
射击区域	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15
1	0.25	0.30	0.20	0.15	0.15	0.15	0.20	0.15	0.20	0.15	0.15	0.15	0.15	0	0
2	0.20	0.20	0.20	0.25	0.10	0.15	0.20	0.10	0.20	0	0.25	0.25	0.10	0	0.10
3	0	0.15	0.30	0.10	0.20	0.25	0.15	0.30	0	0	0.10	0.15	0	0.10	0.15
4	0	0.20	0.15	0.35	0.15	0	0.15	0.25	0.10	0.10	0	0	0.15	0	0.20
5	0.15	0.10	0.20	0	0.25	0.20	0.30	0	0.30	0.25	0.30	0.10	0.30	0.10	0.15
6	0.15	0.15	0.15	0.25	0.30	0	0.25	0.10	0	0.15	0.20	0	0.20	0.20	0.20
7	0.20	0.15	0.25	0	0.20	0.15	0.15	0	0.15	0.20	0.25	0.40	0	0.15	0
8	0.05	0.10	0	0.10	0.15	0.25	0	0.20	0.20	0.20	0.15	0.30	0.15	0.20	0.15
9	0	0	0.25	0.10	0.20	0.15	0.15	0	0.15	0.25	0	0.20	0.25	0.30	0.20
10	0	0	0.15	0.30	0.25	0.20	0.25	0.20	0.10	0.15	0.15	0.20	0.20	0.15	0.35

射击区域	目标
射击区域	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15
1	0.55	0.50	0.45	0.45	0.50	0.20	0.15	0.15	0.25	0.20	0.25	0.35	0.30	0.25	0.20
2	0.30	0.60	0.45	0.30	0.30	0.15	0.25	0.25	0.30	0.45	0.40	0.20	0.20	0.20	0.20
3	0.30	0.50	0.35	0.45	0.45	0.30	0.35	0.30	0.45	0.35	0.40	0.30	0.35	0.45	0.50
4	0.40	0.50	0.55	0.50	0.65	0.30	0.25	0.30	0.40	0.50	0.45	0.55	0.65	0.50	0.40
5	0.25	0.35	0.70	0.40	0.50	0.55	0.35	0.40	0.60	0.45	0.45	0.40	0.45	0.45	0.40
6	0.30	0.25	0.55	0.55	0.40	0.50	0.45	0.55	0.50	0.40	0.55	0.40	0.45	0.50	0.35
7	0.40	0.25	0.20	0.30	0.30	0.40	0.65	0.45	0.45	0.55	0.45	0.35	0.55	0.45	0.45
8	0.30	0.35	0.40	0.55	0.30	0.25	0.50	0.50	0.40	0.55	0.65	0.45	0.45	0.35	0.50
9	0.35	0.20	0.20	0.45	0.30	0.50	0.25	0.55	0.40	0.30	0.35	0.35	0.35	0.60	0.55
10	0.20	0.30	0.30	0.40	0.35	0.45	0.30	0.40	0.55	0.35	0.20	0.55	0.40	0.55	0.70

参数	规模
	小			中			大
	Y₁	Y₂	Y₃	Y₁	Y₂	Y₃	Y₁	Y₂	Y₃
J_max	0.887 0	0.895 3	0.895 8	0.894 4	0.884 5	0.873 7	0.863 7	0.852 9	0.867 6
评价次数	3.6×10³	2.7×10³	3.0×10³	9.8×10¹²	3.8×10¹²	2.8×10¹²	1.6×10³⁴	2.3×10³²	7.1×10³³

规模	战例	指标	ABC-NEH算法	RNADE算法	IABCI算法	标准ABC算法	本文算法
小规模	Y₁	Mean Std SR(100%)/% SR(99%)/%	3.038×10^-4 8.691×10^-4 87 100	9.028×10^-4 1.564×10^-3 73 100	1.244×10^-3 1.562×10^-3 55 100	6.078×10^-3 6.153×10^-3 16 73	1.670×10^-5 1.670×10^-4 99 100
	Y₂	Mean Std SR(100%)/% SR(99%)/%	0 0 100 100	2.781×10^-6· 2.781×10^-5 99 100	0 0 100 100	4.695×10^-3 8.7×10^-3 69 77	0 0 100 100
	Y₃	Mean Std SR(100%)/% SR(99%)/%	1.390×10^-3 3.463×10^-3 86 86	2.305×10^-3 4.523×10^-3 78 78	4.022×10^-3 6.473×10^-3 68 68	1.761×10^-2 9.973×10^-3 12 12	0 0 100 100
中规模	Y₁	Mean Std SR(100%)/% SR(99%)/%	2.087×10^-2 4.989×10^-3 0 1	1.025×10^-2 5.258×10^-3 4 44	2.867×10^-2 6.159×10^-3 0 0	4.176×10^-2 8.021×10^-3 0 0	2.225×10^-3 2.232×10^-3 38 100
	Y₂	Mean Std SR(100%)/% SR(99%)/%	1.631×10^-2 4.683×10^-3 0 8	8.311×10^-3 5.245×10^-3 5 53	2.305×10^-2 5.616×10^-3 0 3	3.578×10^-2 7.128×10^-3 0 0	1.941×10^-3 1.997×10^-3 30 100
	Y₃	Mean Std SR(100%)/% SR(99%)/%	1.593×10^-2 4.513×10^-3 0 10	1.220×10^-2 4.807×10^-3 1 22	2.145×10^-2 4.544×10^-3 0 0	3.139×10^-2 5.690×10^-3 0 0	4.103×10^-3 2.681×10^-3 3 95
大规模	Y₁	Mean Std SR(100%)/% SR(99%)/%	3.768×10^-2 4.868×10^-3 0 0	1.271×10^-2 4.442×10^-3 0 18	4.749×10^-2 5.591×10^-3 0 0	6.188×10^-2 6.032×10^-3 0 0	9.152×10^-3 3.124×10^-3 0 50
	Y₂	Mean Std SR(100%)/% SR(99%)/%	3.310×10^-2 5.156×10^-3 0 0	8.236×10^-3 4.241×10^-3 0 52	4.126×10^-2 5.193×10^-3 0 0	5.349×10^-2 6.716×10^-3 0 0	5.505×10^-3 2.712×10^-3 0 80
	Y₃	Mean Std SR(100%)/% SR(99%)/%	3.452×10^-2 5.583×10^-3 0 0	8.316×10^-3 4.232×10^-3 0 52	4.360×10^-2 6.935×10^-3 0 0	6.034×10^-2 6.880×10^-3 0 0	5.536×10^-3 2.762×10^-3 1 84