火炮火力打击体系多目标优化

doi:10.12382/bgxb.2024.0939

摘要/Abstract

摘要：

针对火炮火力打击体系的构成复杂,各要素相互耦合,传统各分系统相对分离的设计方法难以满足体系融合发展需求的突出问题,构建了基于多目标优化的火力打击体系设计模型。根据火炮火力打击体系的组成,构建多个分系统的参数模型,确定了主要的约束参数和设计参数。建立以任务时间、耗弹量、任务成本为目标的优化设计模型,采用第二代非支配排序遗传算法的多目标遗传算法完成了典型火力体系的优化设计,确定了火力体系构成中无人机、指控设备、火炮、弹药、底盘等分系统的主要设计参数。研究结果表明,多目标优化方法能够解决多个分系统设计中多参数相互耦合的问题,获得体系效能最优的设计结果,并且可根据作战任务需求为火力体系的配置提供支撑。

关键词: 火炮, 火力打击体系, 杀伤链, 任务时间, 体系优化, 多目标优化

Abstract:

Due to the complexity of artillery firepower strike system and the coupling of various elements,the traditional design methods for designing the subsystems to be relatively independent of each other are insufficient to meet the needs of integrated system development.A firepower strike system design model based on multi-objective optimization is constructed to address this issue.According to the composition of artillery firepower strike system,the parameter models for multiple subsystems are established,and the main constraint parameters and the design parameters are determined.An optimization design model is established by taking the mission time,ammunition consumption,and mission cost as the objectives.The multi-objective optimization algorithm of the second-generation non-dominated sorting genetic algorithm (NSGA-II) is used to complete the optimization design of a typical firepower system,thereby determining the main design parameters of subsystems such as drones,command and control equipment,artillery,ammunition,and chassis.The research results show that the multi-objective optimization method can solve the problem of multi-parameter coupling in the design of multiple subsystems,achieve the optimal design results for system performance,and provide support for the configuration of the firepower system according to operational mission requirements.

Key words: artillery, firepower strike system, kill chain, mission time, system optimization, multi-objective optimization

中图分类号:

TJ301

张龙, 钱林方, 马欣宇. 火炮火力打击体系多目标优化[J]. 兵工学报, 2025, 46(8): 240939-.

ZHANG Long, QIAN Linfang, MA Xinyu. Multi-objective Optimization of Artillery Firepower Strike System[J]. Acta Armamentarii, 2025, 46(8): 240939-.

图/表 6

参考文献 22

[1]	JAMES J R, RAGSDALE D, SCHAFER J, et al. Performance modeling of the advanced field artillery tactical data system[C]// Proceedings of the 2000 IEEE International Conference on Systems,Man and Cybernetics.‘Cybernetics Evolving to Systems,Humans,Organizations,and Their Complex Interactions’(Cat.No.0). Nashville,TN,US: IEEE, 2000, 3:2257-2262.
[2]	王玉茜, 曹亚杰, 佘晓琼, 等. 美军杀伤网概念研究及对我防空作战装备体系的启示[J]. 现代防御技术, 2023, 51(6):1-8. doi: 10.3969/j.issn.1009-086x.2023.06.001
	WANG Y Q, CAO Y J, SHE X Q, et al. Research on U.S.military's kill web concept and inspiration to Chinese air defense combat equipment system[J]. Modern Defense Technology, 2023, 51(6):1-8. (in Chinese)
[3]	DARPA. Broad agency announcement:adapting cross-domain kill-webs (ACK) HR001118S0043[EB/OL].(2018-07-20) [2022-10-03]. https://sam.gov/opp/6ef189be7786d06c0d805205e3b0f843/view.
[4]	ANON. Test Flag Enterprise[EB/OL].[2022-10-03]. https://www.aftc.af.mil/Test-Flag-Enterprise/.
[5]	陈登, 陈楚湘, 周春华. 基于OODA环的杀伤网节点重要性评估[J]. 兵工学报, 2024, 45(2):363-372. doi: 10.12382/bgxb.2022.0623
	CHEN D, CHENG C X, ZHOU C H. Importance evaluation of kill network nodes based on OODA loop[J]. Acta Armamentarii, 2024, 45(2):363-372. (in Chinese) doi: 10.12382/bgxb.2022.0623
[6]	FARRELL W J III, WILKENING D. Modeling kill chains probabilistically[J]. Military Operations Research, 2020, 25(3):5-21.
[7]	KELLY P H, LANCE K, BRENDA M, et al. Increased effectiveness of the joint fire's kill chain via improvements in command and control responsiveness for better cooperative engagement capability,AD1126453[R]. Washington,D.C.,US: Systems Engineering Capstone Report, 2020.
[8]	BAILEY A, GLEN C, SYDNEY M, et al. A systems architecture for the indirect fire engagement process in a dynamic targeting scenario[C]// Proceedings of the 2020 Annual General Donald R.Keith Memorial Capstone Conference. New York,NY,US: the Society for Industrial and Systems Engineering, 2020.
[9]	HAN Q, PANG B, LI S, et al. Evaluation method and optimization strategies of resilience for air & space defense system of systems based on kill network theory and improved self-information quantity[J]. Defence Technology, 2023, 21:219-239.
[10]	ISLAM Q N U, AHMED A, ABDULLAH S M. Optimized controller design for islanded microgrid using non-dominated sorting whale optimization algorithm (NSWOA)[J]. Ain Shams Engineering Journal, 2021, 12(4):3677-3689.
[11]	WANG Y, WANG Z H, WANG G G. Hierarchical learning particle swarm optimization using fuzzy logic[J]. Expert Systems with Applications, 2023, 232:120759.
[12]	丁伟, 明振军, 王国新, 等. 基于正向解析式和多目标博弈优化算法的复杂装备体系优化设计方法[J]. 兵工学报, 2024, 45(6):1974-1990. doi: 10.12382/bgxb.2023.0031
	DING W, MING Z J, WANG G X, et al. Optimization design method of complex equipment system-of-systems based on forward analytical formula and MOGT optimization algorithm[J]. Acta Armamentarii, 2024, 45(6):1974-1990. (in Chinese) doi: 10.12382/bgxb.2023.0031
[13]	徐公国, 段修生, 单甘霖, 等. 基于多目标局部变异-自适应量子粒子群优化算法的复杂地形多传感器优化部署[J]. 兵工学报, 2018, 39(11):2192-2201. doi: 10.3969/j.issn.1000-1093.2018.11.013
	XU G G, DUAN X S, SHAN G L, et al. Optimization deployment of multi-sensors in complex terrain based on multi-objective LM-AQPSO algorithm[J]. Acta Armamentarii, 2018, 39(11):2192-2201. (in Chinese)
[14]	RICHARDS C. Boyd's OODA loop[J]. Necesse, 2020, 5(1):142-165.
[15]	JOHN K. Using Al to SAW through OODA loops[J]. Marine Corps Gazette, 2023, 107(8):64-65.
[16]	JOHNSON J. Automating the OODA loop in the age of intelligent machines:reaffirming the role of humans in command-and-control decision-making in the digital age[J]. Defence Studies, 2023, 23(1):43-67.
[17]	CHRISTOPHER K. Conceptualizing information advantage:using Boyd's OODA loop[J]. Military Review, 2022, 102(6):109-115.
[18]	RYDER M, DOWNS C. Rethinking reflective practice:John Boyd's OODA loop as an alternative to Kolb[J]. The International Journal of Management Education, 2022, 20(3):100703.
[19]	LUNDBERG J. Towards a conceptual framework for system of systems[C]// Proceedings of the 35th International Conference on Advanced Information Systems Engineering. Zaragoza,Spain: Springer, 2023.
[20]	CHEN Z W, HONG D P, CUI W W, et al. Resilience evaluation and optimal design for weapon system of systems with dynamic reconfiguration[J]. Reliability Engineering and System Safety, 2023, 237:109409.
[21]	HAI T, ZHOU J C, KHAKI M. Optimal planning and design of integrated energy systems in a microgrid incorporating electric vehicles and fuel cell system[J]. Journal of Power Sources, 2023, 561:232694.
[22]	周健, 龚春林, 粟华, 等. 飞行器体系优化设计问题[J]. 航空学报, 2018, 39(11):222223. doi: 10.7527/S1000-6893.2018.22223
	ZHOU J, GONG C L, SU H, et al. Optimal design problem of system of systems of flight vehicle[J]. Acta Aeronautica et Astronautica Sinica, 2018, 39(11):222223. (in Chinese) doi: 10.7527/S1000-6893.2018.22223

主要设计参数	初始数值	变化范围	参数说明
L_w/m	4	3.5<L_w<5.5	无人机翼展
t_eng/min	20	15<t_eng<35	无人机发动机工作时间
R_p/km	30	25<R_p<40	火炮最大射程
d_p/mm	122	119<d_p<156	火炮口径
R_d/m	50	10<R_d<80	弹丸命中精度
t_pre/s	60	30<t_pre<60	火力指控时间
t_d/s	90	60<t_d<120	弹道飞行时间
t_ev/s	60	30<t_ev<60	火炮撤收时间
Ro_d/(r·min^-1)	6	4<Ro_d<10	射速
v_end/(m·s^-1)	400	300<v_end<500	弹丸末速
S_d/m²	2000	1800<s_d<2800	弹丸杀伤面积

主要设计参数	初始数值	变化范围	参数说明
L_w/m	4	3.5<L_w<5.5	无人机翼展
t_eng/min	20	15<t_eng<35	无人机发动机工作时间
R_p/km	30	25<R_p<40	火炮最大射程
d_p/mm	122	119<d_p<156	火炮口径
R_d/m	50	10<R_d<80	弹丸命中精度
t_pre/s	60	30<t_pre<60	火力指控时间
t_d/s	90	60<t_d<120	弹道飞行时间
t_ev/s	60	30<t_ev<60	火炮撤收时间
Ro_d/(r·min^-1)	6	4<Ro_d<10	射速
v_end/(m·s^-1)	400	300<v_end<500	弹丸末速
S_d/m²	2000	1800<s_d<2800	弹丸杀伤面积

主要设计参数		任务时间最小	耗弹量最小	任务成本最小	加权目标函数最小
目标函数	T_total/s	980.7	1217.7	1507.6	981.5
	B_total/r	6.0	5.0	9.0	5.0
	C_total/万元	83.3	75.8	56.1	84.9
设计变量	L_w/m	5.5	4.6	3.5	5.5
	t_eng/min	27.6	24.2	21.8	26.9
	R_p/km	29.5	27.0	25.2	31.4
	d_p/mm	155	155	130	155
	R_d/m	60.1	35.2	70.3	57.7
	t_pre/s	30.0	32.7	42.9	30.0
	t_d/s	60.1	98.2	71.4	60.0
	t_ev/s	30.1	44.6	32.0	31.1
	Ro_d/(r·min^-1)	10.0	5.5	9.9	9.2
	v_end/(m·s^-1)	419.6	355.4	370.5	424.1
	S_d/m²	2510.0	2658.3	1829.7	2658.6

主要设计参数		任务时间最小	耗弹量最小	任务成本最小	加权目标函数最小
目标函数	T_total/s	980.7	1217.7	1507.6	981.5
	B_total/r	6.0	5.0	9.0	5.0
	C_total/万元	83.3	75.8	56.1	84.9
设计变量	L_w/m	5.5	4.6	3.5	5.5
	t_eng/min	27.6	24.2	21.8	26.9
	R_p/km	29.5	27.0	25.2	31.4
	d_p/mm	155	155	130	155
	R_d/m	60.1	35.2	70.3	57.7
	t_pre/s	30.0	32.7	42.9	30.0
	t_d/s	60.1	98.2	71.4	60.0
	t_ev/s	30.1	44.6	32.0	31.1
	Ro_d/(r·min^-1)	10.0	5.5	9.9	9.2
	v_end/(m·s^-1)	419.6	355.4	370.5	424.1
	S_d/m²	2510.0	2658.3	1829.7	2658.6