Air Defense Missile Weapon Target Assignment Based on Multi-objective Evolutionary Algorithm

doi:10.16182/j.issn1004731x.joss.24-0118

Abstract

Abstract:

An effective weapon target assignment method can reduce the combat losses and improve the defense effect. A reasonable mathematical model is established for the allocation of air defense resources, aiming at the optimization objectives of maximizing target destruction effectiveness and minimizing radar resource consumption, considering multiple constraints such as the upper limit of radar channels, on the basis of multiobjective evolutionary algorithm based on decomposition (MOEA/D), the probability of crossover and mutation is adaptively adjusted to improve the quality of individuals in the process of population evolution, and a set of optimal solution sets for decision makers is obtained. The results show that, compared with other multi-objective evolutionary algorithms, the algorithm can obtain the higher fitness values and good distributivity, and can provide a feasible solution to the weapon target assignment for air defense missile.

Key words: weapon-target assignment, multi-objective evolutionary algorithm, adaptive parameter, air defence missile

CLC Number:

TP.391.9

Sun Xin, Xing Lining, Wang Rui, Wang Ling, Shi Jianmai, Luo Tianyu. Air Defense Missile Weapon Target Assignment Based on Multi-objective Evolutionary Algorithm[J]. Journal of System Simulation, 2024, 36(6): 1298-1308.

Figures/Tables 10

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Table 1

Parameters setting of algorithm

参数	参数值	描述
p	200	种群规模
G	200	最大迭代次数
$M$	24/60	来袭目标个数
$C 0$	$7 / M$	需要交叉的基因概率
$M 0$	$2 / M$	需要变异的基因概率
η	1	概率控制参数

Table 1

Table 2

Radar resource consumption of different algorithms for maximum target damage effectiveness

规模	算法	目标毁伤效能	雷达资源消耗
$M = 24$	GA	57.7	94.70
	NSGA-II	61.7	71.70
	MOEA/D	60.7	78.20
	MOEA/D-AD	62.7	71.05
$M = 60$	GA	113.6	139.80
	NSGA-II	133.1	118.10
	MOEA/D	133.9	127.60
	MOEA/D-AD	143.4	112.60

Table 2

Fig. 5

Fig. 6

Fig. 7

Fig. 8

References 25

1	Lloyd S P, Witsevhausden H S. Weapon Allocation Is NP-complete[C]//Proc of the IEEE Summer Simulation Conference. Piscataway, NJ, USA: IEEE, 1986: 1054-1058.
2	Mehmet Fatih Hocaoğlu. Weapon Target Assignment Optimization for Land Based Multi-air Defense Systems: A Goal Programming Approach[J]. Computers & Industrial Engineering, 2019, 128: 681-689.
3	Yao Zongxin, Li Ming, Chen Zongji, et al. Mission Decision-making Method of Multi-aircraft Cooperatively Attacking Multi-target Based on Game Theoretic Framework[J]. Chinese Journal of Aeronautics, 2016, 29(6): 1685-1694.
4	Kline A G, Ahner D K, Lunday B J. Real-time Heuristic Algorithms for the Static Weapon Target Assignment Problem[J]. Journal of Heuristics, 2019, 25(3): 377-397.
5	Lu Yiping, Chen D Z. A New Exact Algorithm for the Weapon-target Assignment Problem[J]. Omega, 2021, 98: 102138.
6	Ji Eun Kim, Chang Hun Lee, Yong Yi Mun. New Weapon Target Assignment Algorithms for Multiple Targets Using a Rotational Strategy and Clustering Approach[J]. IEEE Access, 2022, 10: 43738-43750.
7	Chang Xuening, Shi Jianmai, Luo Zhihao, et al. Adaptive Large Neighborhood Search Algorithm for Multi-stage Weapon Target Assignment Problem[J]. Computers & Industrial Engineering, 2023, 181: 109303.
8	Yao Feng, Du Yonghao, Li Lei, et al. General Modeling and Optimization Technique for Real-world Earth Observation Satellite Scheduling[J]. Frontiers of Engineering Management, 2023, 10(4): 695-709.
9	Wang Yuting, Han Yuyan, Gong Dunwei, et al. A Review of Intelligent Optimization for Group Scheduling Problems in Cellular Manufacturing[J]. Frontiers of Engineering Management, 2023, 10(3): 406-426.
10	Xing Lining, Rohlfshagen P, Chen Yingwu, et al. A Hybrid Ant Colony Optimization Algorithm for the Extended Capacitated Arc Routing Problem[J]. IEEE Transactions on Systems Man and Cybernetics Part B-Cybernetics, 2011, 41(4): 1110-1123.
11	Li Wei, Jing Jianghui, Chen Yangtao, et al. Evolutionary Experience-driven Particle Swarm Optimization with Dynamic Searching[J]. Complex System Modeling and Simulation, 2023, 3(4): 307-326.
12	Guo Dong, Liang Zixuan, Jiang Peng, et al. Weapon-target Assignment for Multi-to-multi Interception with Grouping Constraint[J]. IEEE Access, 2019, 7: 34838-34849.
13	Hu Xinwu, Luo Pengcheng, Zhang Xiaonan, et al. Improved Ant Colony Optimization for Weapon-target Assignment[J]. Mathematical Problems in Engineering, 2018, 2018: 6481635.
14	陈曼, 周凤星. 改进粒子群算法的舰载武器目标分配[J]. 火力与指挥控制, 2018, 43(11): 72-76.
	Chen Man, Zhou Fengxing. Shipborne Weapon Target Assignment Based on Improved Particle Swarm Optimization[J]. Fire Control & Command Control, 2018, 43(11): 72-76.
15	周凌超. 基于改进模拟退火算法的导弹目标分配方法[J]. 工业控制计算机, 2018, 31(1): 95-97.
	Zhou Lingchao. Target Assignment for Missile Based on Improved Simulated Annealing Algorithm[J]. Industrial Control Computer, 2018, 31(1): 95-97.
16	Wu Xiaochen, Chen Chen, Ding Shuxin. A Modified MOEA/D Algorithm for Solving Bi-objective Multi-stage Weapon-target Assignment Problem[J]. IEEE Access, 2021, 9: 71832-71848.
17	陈思, 胡涛. 基于多目标优化遗传算法的武器-目标分配[J]. 舰船电子工程, 2015, 35(7): 54-57, 100.
	Chen Si, Hu Tao. Weapon-target Assignment with Multi-objective Non-dominated Set Ranking Genetic Algorithm[J]. Ship Electronic Engineering, 2015, 35(7): 54-57, 100.
18	于博文, 吕明. 基于改进NSGA-III算法的动态武器协同火力分配方法[J]. 火力与指挥控制, 2021, 46(8): 71-77, 82.
	Yu Bowen, Ming Lü. Method for Dynamic Weapon Coordinative Firepower Distribution Based on Improved NSGA‑Ⅲ Algorithm[J]. Fire Control & Command Control, 2021, 46(8): 71-77, 82.
19	Xu Wenqin, Chen Chen, Ding Shuxin, et al. A Bi-objective Dynamic Collaborative Task Assignment Under Uncertainty Using Modified MOEA/D with Heuristic Initialization[J]. Expert Systems with Application, 2020, 140(2): 112844.1-112844.24
20	邱少明, 冯江惠, 杜秀丽. 基于改进多目标HQPSOGA求解武器目标分配问题[J]. 计算机应用与软件, 2021, 38(11): 255-262.
	Qiu Shaoming, Feng Jianghui, Du Xiuli. Weapon Target Assignment Based On Improved Multi-Objective HQPSOGA[J]. Computer Applications and Software, 2021, 38(11): 255-262.
21	Yi Xiaojian, Yu Huiyang, Xu Tao. Solving Multi-objective Weapon-target Assignment Considering Reliability by Improved MOEA/D-AM2M[J]. Neurocomputing, 2024, 563: 126906.
22	Zhang Qingfu, Li Hui. MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition[J]. IEEE Transactions on Evolutionary Computation, 2007, 11(6): 712-731.
23	Yi Jiaohong, Xing Lining, Wang Gaige, et al. Behavior of Crossover Operators in NSGA-III for Large-scale Optimization Problems[J]. Information Sciences, 2020, 509: 470-487.
24	Zhou Shengchao, Xing Lining, Zheng Xu, et al. A Self-adaptive Differential Evolution Algorithm for Scheduling a Single Batch-processing Machine with Arbitrary Job Sizes and Release Times[J]. IEEE Transactions on Cybernetics, 2021, 51(3): 1430-1442.
25	Deb K, Pratap A, Agarwal S, et al. A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II[J]. IEEE Transactions on Evolutionary Computation, 2002, 6(2): 182-197.

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