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

摘要/Abstract

摘要：

针对多无人机空战目标分配问题, 提出一种基于改进能量谷优化的多无人机空战目标分配方法。首先, 建立无人机空战态势评估模型, 对多无人机空战目标分配问题进行数学建模。然后, 在能量谷优化算法的基础上, 对算法中粒子衰变过程进行改进, 使能量谷优化算法适用于离散型优化问题, 并提出混沌映射初始化和二进制改进两种改进措施。最后, 分别开展消融实验与有效性实验对所提方法的有效性进行验证。仿真结果表明, 所提改进措施对于在无人机目标分配任务中能量谷优化算法的算法效果具有一定的提升, 且改进能量谷优化算法能够适用于多无人机空战目标分配问题, 在多无人机空战中具有一定的应用意义。

关键词: 多无人机空战, 目标分配, 能量谷优化, 态势评估

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

中图分类号:

V279

张嘉辉, 蒙志君, 何家政, 王子琛, 林尤深. 基于改进能量谷优化的多无人机空战目标分配[J]. 系统工程与电子技术, 2024, 46(11): 3754-3763.

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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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