基于改进能量谷优化的多无人机空战目标分配

doi:10.12305/j.issn.1001-506X.2024.11.17

Abstract

Abstract:

To solve the problem of multi-unmanned aerial vehicle (UAV) air combat target allocation, this paper proposes a multi-UAV air combat target allocation method based on improved energy valley optimization. Firstly, the UAV air combat situation assessment model is established, and the multi-UAV air combat target allocation problem is mathematically modeled based on the UAV air combat situation assessment model. Then, on the basis of the energy valley optimization algorithm, the particle decay process in the algorithm is improved, so that the energy valley optimization algorithm is suitable for discrete optimization problems. Two improvement measures, namely chaotic mapping and binary improvement, are proposed. Finally, elimination experiments and effectiveness experiments are designed to verify the effectiveness of the proposed method. The simulation results show that the proposed improvement measures enhance the algorithm performance of energy valley optimization algorithm in UAV target allocation. Moreover, the improved energy valley optimization algorithm can be applied to solve the multi-UAV air combat target allocation problem, and has certain application significance in multi-UAV air combat.

Key words: multi-unmanned aerial vehicle (UAV) air combat, target allocation, energy valley optimization, situation assessment

CLC Number:

V279

Jiahui ZHANG, Zhijun MENG, Jiazheng HE, Zichen WANG, Youshen LIN. Multi-UAV air combat target allocation based on improved energy valley optimization[J]. Systems Engineering and Electronics, 2024, 46(11): 3754-3763.

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References 20

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编号	T1	T2	T3	T4
U1	0.204	0.274	0.357	0.202
U2	0.245	0.342	0.407	0.246
U3	0.175	0.224	0.274	0.162
U4	0.266	0.313	0.428	0.266

Tent映射	二进制改进	最优值	平均值	最差值	标准差	收敛步数
-	-	1.147 6	1.125 4	1.099 5	0.016 5	60
√	-	1.147 6	1.129 8	1.086 5	0.017 8	55
-	√	1.147 6	1.136 6	1.108 5	0.013 6	6
√	√	1.147 6	1.142 0	1.086 5	0.015 2	14

编号	T1	T2	T3	T4	T5	T6
U1	0.220	0.323	0.413	0.415	0.364	0.266
U2	0.256	0.367	0.512	0.491	0.484	0.276
U3	0.200	0.282	0.325	0.340	0.249	0.286
U4	0.263	0.390	0.531	0.514	0.490	0.283

算法	最优值	平均值	最差值	标准差	收敛步数	收敛仿真时间/s
离散ACO	1.741 8	1.420 6	1.005 7	0.192 0	31	0.237
离散GA	1.797 6	1.657 0	1.272 5	0.123 4	12	0.036
改进BEVO	1.797 6	1.790 8	1.729 0	0.013 6	41	0.274

无人机	目标
U1	T4
U2	T3
U3	T4
U4	T3

Multi-UAV air combat target allocation based on improved energy valley optimization

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算法	最优值	平均值	最差值	标准差	收敛步数	收敛仿真时间/s
离散ACO	1.867 1	0.982 5	0.003 6	0.528 4	24	0.164
离散GA	1.921 5	1.740 3	1.435 6	0.114 4	8	0.032
改进BEVO	1.939 3	1.861 6	1.808 3	0.033 4	42	0.283