An Improved Particle Swarm Optimization Algorithm for Optimizing the Aiming Point of Multiple Projectiles against Surface Targets

doi:10.12382/bgxb.2024.0894

Abstract

Abstract:

An improved particle swarm optimization (IPSO) algorithm is proposed to optimize the optimal aiming point of multiple projectiles against complex-shaped surface targets.When constructing an aiming point selection model,the influences of complex factors such as surface target area shape,regional correlation,ammunition power area,ammunition hit accuracy,cumulative damage and multi-projectiles combined damage on target damage effect are considered comprehensively.Particle swarm optimization (PSO) algorithm is improved by preassigning the particle positions and introducing the particle activation energy,which not only improves the convergence speed of the algorithm but also ensures the global search ability.The proposed algorithm is verified through typical complex target test cases.The results show that,compared with Monte Carlo algorithm,PSO algorithm and improved grey wolf optimization algorithm,the IPSO algorithm has a better ability to select aiming points,and increases the average damage yield by 4.3%.And the average computation time for aiming point selection is only 1/4-1/3 of that of traditional optimization algorithms.,which has obvious advantages in damage income and computing efficiency.

Key words: multiple ammunition, improved particle swarm optimization, aiming point selection, cumulative damage, combined damage, complex surface target

YIN Peng, HUANG Fenglei, SHI Keren, YAN Xuefei, LIU Yan, YAN Jiang, YU Jie. An Improved Particle Swarm Optimization Algorithm for Optimizing the Aiming Point of Multiple Projectiles against Surface Targets[J]. Acta Armamentarii, 2025, 46(9): 240894-.

Figures/Tables 11

References 27

[1]	任春艳, 刘颖, 张迁, 等. 2022年国外高超声速空面导弹发展动态研究[J]. 航空兵器, 2023, 30(6):18-26.
	REN C Y, LIU Y, ZHANG Q, et al. Research on the development trends of hypersonic air to surface missiles abroad in 2022[J]. Aero Weaponry, 2023, 30(6):18-26. (in Chinese)
[2]	高天运, 马兰, 齐建成. 外军高超声速武器作战及其目标杀伤链构建分析[J]. 战术导弹技术, 2024, 45(3):136-147.
	GAO T Y, MA L, QI J C. Analysis of hypersonic weapon operations and target kill chain construction in foreign armies[J]. Tactical Missile Technology, 2024, 45(3):136-147. (in Chinese)
[3]	MOON S H. Weapon effectiveness and the shapes of damage functions[J]. Defence Technology, 2021, 17(2):617-632. doi: 10.1016/j.dt.2020.04.009
[4]	卢发兴, 贾正荣, 王航宇, 等. 对任意分布目标的舰炮对面区域射击瞄准点配置方法[J]. 系统工程与电子技术, 2019, 41(6):1278-1285. doi: 10.3969/j.issn.1001-506X.2019.06.15
	LU F X, JIA Z R, WANG H Y, et al. Method for configuring aiming points for shooting in the opposite area of naval guns with arbitrary distribute-on targets[J]. System Engineering and Electronics, 2019, 41(6):1278-1285. (in Chinese)
[5]	周于翔, 舒健生, 郑晓龙, 等. 基于改进遗传算法的多弹混合瞄准点优化[J]. 指挥控制与仿真, 2023, 45(1):68-74. doi: 10.3969/j.issn.1673-3819.2023.01.012
	ZHOU Y X, SHU J S, ZHENG X L, et al. Optimization of multi missile hybrid aiming points based on improved genetic algorithm[J]. Command Control & Simulation, 2023, 45(1):68-74. (in Chinese)
[6]	李臣明, 宦超, 石怀龙. 某箱式火箭炮对面目标分布式杀伤最优火力分配[J]. 兵工学报, 2017, 38(9):1699-1704. doi: 10.3969/j.issn.1000-1093.2017.09.005
	LI C M, HUAN C, SHI H L. Optimal firepower allocation for distributed killing of targets on the opposite side of a box type rocket launcher[J]. Acta Armamentarii, 2017, 38(9):1699-1704. (in Chinese)
[7]	徐豫新, 贾志远, 杨晓红, 等. 应用差分进化-神经网络模型的杀爆弹瞄准点分配方法[J]. 北京理工大学学报, 2024, 44(2):146-155.
	XU Y X, JIA Z Y, YANG X H, et al. Application of differential evolution neural network model for target point allocation of explosive ordnance[J]. Transactions of Beijing Institute of Technology, 2024, 44(2):146-155. (in Chinese)
[8]	佘维, 马凯, 田钊, 等. 基于改进灰狼优化算法的地面目标最优瞄准点选择方法[J]. 火力与指挥控制, 2023, 48(3):139-145.
	SHE W, MA K, TIAN Z, et al. Optimal aiming point selection method for ground targets based on improved grey wolf optimization algorithm[J]. Fire Control & Command Control, 2023, 48(3):139-145. (in Chinese)
[9]	ZHANG H, ZHANG X J, SONG S M, et al. An affinity propagation-based multi-objective evolutionary algorithm for selecting optimal aiming points of missiles[J]. Soft Computing, 2017, 21(11):3013-3031.
[10]	侯鹏, 裴扬, 张睿文, 等. 地空导弹破片式打击军机的瞄准点选择方法[J]. 北京航空航天大学学报, 2023, 49(6):1434-1445.
	HOU P, PEI Y, ZHANG R W, et al. Method for selecting aiming points for ground to air missile fragmentation strikes on military aircraft[J]. Journal of Beijing University of Aeronautics and Astronautics, 2023, 49(6):1434-1445. (in Chinese)
[11]	侯鹏, 裴扬, 赵倩, 等. 典型机场目标的毁伤效果研究[J]. 航空科学技术, 2023, 34(12):100-110.
	HOU P, PEI Y, ZHAO Q, et al. Research on the destructive effect of typical airport targets[J]. Aeronautical Science and Technology, 2023, 34(12):100-110. (in Chinese)
[12]	晏江, 尹鹏, 刘彦, 等. 联合弹药毁伤复杂面目标瞄准点高效优化算法[J]. 兵工学报, 2025, 46(4):240172. doi: 10.12382/bgxb.2024.0172
	YAN J, YIN P, LIU Y, et al. Efficient optimization algorithm for targeting points of complex targets in joint ammunition destruction[J]. Acta Armamentarii, 2025, 46(4):240172. (in Chinese)
[13]	冯富强, 王兆纶, 欧渊, 等. 一种非均匀面目标毁伤效果计算方法[J]. 北京理工大学学报, 2023, 43(9):895-902.
	FENG F Q, WANG Z L, OU Y, et al. A method for calculating the damage effect of non-uniform surface targets[J]. Journal of Beijing Institute of Technology, 2023, 43(9):895-902. (in Chinese)
[14]	杨奇松, 王然辉, 王顺宏, 等. 基于向量网格法评估面目标毁伤效果[J]. 火力与指挥控制, 2017, 42(6):175-178,182.
	YANG Q S, WANG R H, WANG S H, et al. Evaluation of surface target damage effect based on vector grid method[J]. Fire Control & Command Control, 2017, 42(6):175-178,182. (in Chinese)
[15]	宋谢恩, 王伟鹏, 丁锋, 等. 某型弹道修正弹落点散布规律研究[J]. 弹道学报, 2021, 33(2):40-46,54. doi: 10.12115/j.issn.1004-499X(2021)02-006
	SONG X E, WANG W P, DING F, et al. Research on the dispersion law of the landing point of a certain type of ballistic correction projectile[J]. Journal of Ballistics, 2021, 33(2):40-46,54. (in Chinese)
[16]	LI H S. Recognition model and algorithm of projectiles by combining particle swarm optimization support vector and spatial-temporal constrain[J]. Defence Technology, 2023, 27(9):273-283.
[17]	张良力, 马晓凤. 基于改进粒子群算法的新能源汽车充电站选址方法[J]. 吉林大学学报(工学版), 2024, 54(8):2275-2281.
	ZHANG L L, MA X F. Site selection method for new energy vehicle charging stations based on improved particle swarm optimization algorithm[J]. Journal of Jilin University (Engineering Edition), 2024, 54(8):2275-2281. (in Chinese)
[18]	王四季, 张羽薇, 黄开明, 等. 基于改进粒子群算法的直升机动力涡轮转子系统优化方法[J]. 航空学报, 2024, 45(1):242-258.
	WANG S J, ZHANG Y W, HUANG K M, et al. Optimization method for helicopter power turbine rotor system based on improved particle swarm optimization algorithm[J]. Acta Aeronautica et Astronautica Sinica, 2024, 45(1):242-258. (in Chinese)
[19]	陈涛, 周志刚, 雷楠南. 粒子群算法下汽车机械式自动变速系统参数多目标优化[J]. 吉林大学学报(工学版), 2024, 54(5):1214-1220.
	CHEN T, ZHOU Z G, LEI N N. Multi objective optimization of parameters for automotive mechanical automatic transmission system using particle swarm optimization algorithm[J]. Journal of Jilin University (Engineering Edition), 2024, 54(5):1214-1220. (in Chinese)
[20]	WU Z D, LUO Y S, HU S L. Optimization of jamming formation of USV offboard active decoy clusters based on an improved PSO algorithm[J]. Defence Technology, 2024, 32(2):529-540.
[21]	TANG X Y, WANG P, ZHANG Z Y, et al. Collaborative optimization of exhaust gas recirculation and Miller cycle of two-stage turbocharged marine diesel engines based on particle swarm optimization[J]. Journal of Central South University, 2022, 29(7):2142-2156.
[22]	WANG J F, JIA G W, LIN J C, et al. Cooperative task allocation for heterogeneous multi-UAV using multi-objective optimization algorithm[J]. Journal of Central South University, 2020, 27(2):432-448.
[23]	代权, 李向东, 周兰伟, 等. 基于多矩形饼切函数的弹药毁伤效能评估方法[J]. 爆炸与冲击, 2024, 44(3):52-62.
	DAI Q, LI X D, ZHOU L W, et al. An assessment method for ammunition damage effectiveness based on multiple rectangular cookie cutter function[J]. Explosion and Shock Waves, 2024, 44(3):52-62. (in Chinese)
[24]	尹建平, 王志军. 弹药学[M]. 第2版. 北京: 北京理工大学出版社, 2012:313-325.
	YIN J P, WANG Z J. Ammunition[M]. 2nd ed. Beijing: Beijing Institute of Technology Press, 2012:313-325. (in Chinese)
[25]	刘建斌, 徐豫新, 高鹏, 等. 火箭杀爆弹毁伤幅员仿真[J]. 兵工学报, 2016, 37(增刊2):159-164.
	LIU J B, XU Y X, GAO P, et al. Simulation on damage area of high explosive projectile[J]. Acta Armamentarii, 2016, 37(S2):159-164. (in Chinese)
[26]	MIRJALILI S, IRJALILI S M, EWIS A. Grey wolf optimizer[J]. Advances in Engineering Software, 2014, 69(3):46-61.
[27]	NADIMI S H, TAGHIAN S, MIRJALILI S. An improved grey wolf optimizer for solving engineering problems[J]. Expert Systems with Applications, 2021, 166:113917.

弹药类型	威力区域	描述	CEP/m
1		直径为12m的圆形	6
2		长轴为20m、短轴为12m 的椭圆形	12

弹药类型	威力区域	描述	CEP/m
1		直径为12m的圆形	6
2		长轴为20m、短轴为12m 的椭圆形	12

编号	I_c	I_a	D_c	D_a	C_l
1	0.05	0.10	0.02	0.10
2	0.50	0.30	0.60	0.20
3	0.70	0.25	0.50	0.15	(1,0.32)
4	0.70	0.25	0.50	0.14	(1,0.32)
5	0.65	0.30	0.70	0.30	(12,0.50)
6	0.75	0.35	0.80	0.33	(4,0.45)
7	0.75	0.34	0.80	0.34	(4,0.44)
8	0.62	0.35	0.71	0.23	(8,0.25)
9	0.66	0.45	0.69	0.19
10	0.66	0.45	0.69	0.19
11	0.80	0.35	0.77	0.31	(8,0.25),(11,0.35)
12	0.81	0.25	0.59	0.43
13	0.84	0.25	0.59	0.43
14	0.92	0.44	0.70	0.47	(12,0.30),(14,0.28),(11,0.15)
15	0.52	0.25	0.59	0.43	(11,0.32)
16	0.79	0.44	0.57	0.47	(17,0.30)
17	0.71	0.41	0.59	0.45
18	0.66	0.35	0.64	0.30

编号	I_c	I_a	D_c	D_a	C_l
1	0.05	0.10	0.02	0.10
2	0.50	0.30	0.60	0.20
3	0.70	0.25	0.50	0.15	(1,0.32)
4	0.70	0.25	0.50	0.14	(1,0.32)
5	0.65	0.30	0.70	0.30	(12,0.50)
6	0.75	0.35	0.80	0.33	(4,0.45)
7	0.75	0.34	0.80	0.34	(4,0.44)
8	0.62	0.35	0.71	0.23	(8,0.25)
9	0.66	0.45	0.69	0.19
10	0.66	0.45	0.69	0.19
11	0.80	0.35	0.77	0.31	(8,0.25),(11,0.35)
12	0.81	0.25	0.59	0.43
13	0.84	0.25	0.59	0.43
14	0.92	0.44	0.70	0.47	(12,0.30),(14,0.28),(11,0.15)
15	0.52	0.25	0.59	0.43	(11,0.32)
16	0.79	0.44	0.57	0.47	(17,0.30)
17	0.71	0.41	0.59	0.45
18	0.66	0.35	0.64	0.30

编号	I_c	I_a	D_c	D_a	C_l
1	0.05	0.10	0.02	0.10
2	0.50	0.30	0.60	0.20
3	0.70	0.25	0.50	0.15	(1,0.32)
4	0.70	0.25	0.50	0.14
5	0.65	0.30	0.70	0.30
6	0.75	0.35	0.80	0.33
7	0.75	0.34	0.80	0.34
8	0.62	0.35	0.71	0.23	(8,0.25),(9,0.35)
9	0.66	0.45	0.69	0.19
10	0.66	0.45	0.69	0.19
11	0.80	0.35	0.77	0.31
12	0.81	0.25	0.59	0.43
13	0.84	0.25	0.59	0.43
14	0.92	0.44	0.70	0.47	(12,0.40),(14,0.28)
15	0.52	0.25	0.59	0.43
16	0.79	0.44	0.57	0.47
17	0.71	0.41	0.59	0.45	(17,0.25)
18	0.66	0.35	0.64	0.30