Dynamic Weapon-target Assignment Based on Three-way Decisionand Genetic Algorithm

doi:10.12382/bgxb.2024.0281

Abstract

Abstract:

A multi-stage decision problem of the dynamic weapon-target assignment (DWTA)is modelled and solved based on a game idea.For determining the targets to be attacked in dynamic multi-stage,the three-way decision theory is introduced to improve the threat assessment model,and the priority targets to be attacked in each stage are selected for firepower assignment through the acceptance,delay and reject decisions.A dynamic weapon-target assignment model based on the game idea is constructed,and the objective function of enemy avoidance risk value is designed to take into account the intelligence and uncertainty of the incoming target.For the influence of decision preference in multi-stage solution,a dynamic selection mechanism combining the crowding degree and reference point is designed to cope with the preference of different objective functions in dynamic situations and improve the solution quality.The improved algorithm is used to solve the proposed DWTA problem,and the effectiveness of the proposed algorithm is verified through comparative simulation experiments.

Key words: weapon-target assignment, three-way decision, multi-objective optimization, genetic algorithm, crowding degree

LIU Fuqiang, ZHOU Lun, LIU Zhongyang, PI Yangjun, PU Huayan, LUO Jun. Dynamic Weapon-target Assignment Based on Three-way Decisionand Genetic Algorithm[J]. Acta Armamentarii, 2025, 46(3): 240281-.

Figures/Tables 7

Table 1 Loss function in each case

行动	状态
行动	A	$A ¯$
a_P	λ_PP	λ_PN
a_B	λ_BP	λ_BN
a_N	$λ N P$	λ_NN

Table 1 Loss function in each case

行动	状态
行动	A	$A ¯$
a_P	λ_PP	λ_PN
a_B	λ_BP	λ_BN
a_N	$λ N P$	λ_NN

Fig.1 Grid crowding density

Table 2 The scale of the defense scenario

场景规模	防御武器数量	来袭目标数量
1	5	10
2	15	20

Table 3 Hypervolume indexes for the solution sets obtained by the five algorithms

场景		NSGA-Ⅲ	PAGA	IHGA	ANSGA- Ⅲ	DC- NSGA-Ⅲ
规模1	最好值	0.3576	0.4055	0.4055	0.3871	0.4131
	最差值	0.3142	0.3000	0.3504	0.3045	0.3570
	平均值	0.3416	0.3462	0.3750	0.3772	0.3829
	方差值	3.42e-04	1.57e-03	4.79e-04	3.17e-04	5.40e-04
规模2	最好值	0.2355	0.1951	0.2437	0.2552	0.2596
	最差值	0.2002	0.1659	0.1594	0.1883	0.2008
	平均值	0.2148	0.1846	0.1896	0.2214	0.2327
	方差值	1.73e-04	1.46e-04	1.17e-03	1.24e-04	6.31e-04

Table 4 Dominant coverages of the solution sets obtained by the five algorithms

场景	X₁	X₂
场景	X₁	S₁	S₂	S₃	S₄	S₅
规模1	S₁	0.0000	0.3711	0.1789	0.1983	0.1515
	S₂	0.1400	0.0000	0.1579	0.2046	0.1010
	S₃	0.2200	0.1577	0.0000	0.1547	0.2323
	S₄	0.2500	0.2154	0.1846	0.0000	0.2472
	S₅	0.2600	0.4433	0.2947	0.3142	0.0000
规模2	S₁	0.0000	0.2232	0.3568	0.1167	0.0534
	S₂	0.3035	0.0000	0.2562	0.1674	0.1336
	S₃	0.1262	0.1321	0.0000	0.2763	0.0486
	S₄	0.1664	0.2157	0.3349	0.0000	0.2075
	S₅	0.3214	0.3472	0.5028	0.3194	0.0000

Fig.2 Convergence curves of objective functions of the five algorithms under Scale 1

Fig.3 Convergence curves of each objective function of the five algorithms under Scale 2

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