基于经验自学习人工蜂群算法的干扰资源分配

doi:10.12305/j.issn.1001-506X.2025.12.11

系统工程与电子技术 ›› 2025, Vol. 47 ›› Issue (12): 4093-4100.doi: 10.12305/j.issn.1001-506X.2025.12.11

• 传感器与信号处理 • 上一篇

基于经验自学习人工蜂群算法的干扰资源分配

赵忠凯¹^,²^,*(), 黄馨瑶¹, 郑沛³, 李虎³

1. 哈尔滨工程大学信息与通信工程学院，黑龙江哈尔滨 150001
2. 哈尔滨工程大学先进船舶通信与信息技术工业和信息化部重点实验室，黑龙江哈尔滨 150001
3. 北京航天长征飞行器研究所，北京 100076

收稿日期:2024-04-29 修回日期:2024-08-19 出版日期:2025-03-20 发布日期:2025-03-20
通讯作者: 赵忠凯 E-mail:448933663@qq.com
作者简介:黄馨瑶（2000—），女，硕士研究生，主要研究方向为干扰资源分配
郑　沛（1986—），男，高级工程师，博士，主要研究方向为雷达电子对抗
李　虎（1986—），男，高级工程师，博士，主要研究方向为雷达电子对抗
基金资助:
国家自然科学基金（62071137）资助课题

Interference resource allocation based on empirical self-learning artificial bee colony algorithm

Zhongkai ZHAO¹^,²^,*(), Xinyao HUANG¹, Pei ZHENG³, Hu LI³

1. College of Information and Communication Engineering，Harbin Engineering University，Harbin 150001，China
2. Key Laboratory of Advanced Marine Communication and Information Technology，Ministry of Industry and Information Technology，Harbin Engineering University，Harbin 150001，China
3. Beijing Aerospace Long March Aircraft Research Institute，Beijing 100076，China

Received:2024-04-29 Revised:2024-08-19 Online:2025-03-20 Published:2025-03-20
Contact: Zhongkai ZHAO E-mail:448933663@qq.com
Supported by:
The National Natural Science Foundation of China (62071137)

摘要/Abstract

摘要：

针对传统人工蜂群算法易陷入局部最优解、进化盲目等问题，提出经验自学习概念，并将其与人工蜂群算法相结合，实现经验自学习人工蜂群(empirical self-learning artificial bee colony algorithm，ESLABC)算法。基于熵权法对雷达威胁进行评估，对多约束干扰资源分配模型构建干扰效益评估函数，利用ESLABC算法进行仿真求解。仿真和实验结果表明， ESLABC算法拥有全局寻优能力强、收敛速度快、鲁棒性好等优点，有效解决了集群协同干扰场景下的干扰资源分配问题。

关键词: 人工蜂群算法, 经验自学习, 干扰效果评估, 干扰资源分配

Abstract:

In order to solve the problems of local optimal solution and blind evolution of traditional artificial bee colony algorithm, a concept of experiential self-learning is proposed, which is combined with artificial bee colony algorithm, to implement empirical self-learning artificial bee colony （ESLABC） algorithm. Based on the entropy weight method, the radar threat is evaluated, and the interference benefit evaluation function is constructed for the multi-constraint inteference resource allocation model, and the ESLABC algorithm is used for simulation solution. Simulation and experimental results show that the ESLABC algorithm has the advantages of strong global optimization, fast convergence and good robustness, and effectively solves the problem of interference resource allocation in the cluster cooperative interference scenario.

Key words: artificial bee colony algorithm, experiential self-learning, inteference effect evaluation, inteference resource allocation

中图分类号:

TN 974

赵忠凯, 黄馨瑶, 郑沛, 李虎. 基于经验自学习人工蜂群算法的干扰资源分配[J]. 系统工程与电子技术, 2025, 47(12): 4093-4100.

Zhongkai ZHAO, Xinyao HUANG, Pei ZHENG, Hu LI. Interference resource allocation based on empirical self-learning artificial bee colony algorithm[J]. Systems Engineering and Electronics, 2025, 47(12): 4093-4100.

图/表 8

图1

图2

表1

表2

图3

图4

图5

图6

参考文献 30

1	LU D J, WANG X, WU X T, et al. Adaptive allocation strategy for cooperatively jamming netted radar system based on improved cuckoo search algorithm[J]. Defence Technology, 2023, 24 (6): 285- 297.
2	沈阳, 陈永光, 李修和. 基于0-1规划的雷达干扰资源优化分配研究[J]. 兵工学报, 2007, 28 (5): 528- 532.
	SHEN Y, CHEN Y G, LI X H. Optimal allocation of radar jamming resources based on 0-1 programming[J]. Acta Armamentarii, 2007, 28 (5): 528- 532.
3	吕永胜, 王树宗, 王向伟, 等. 基于贴近度的雷达干扰资源分配策略研究[J]. 系统工程与电子技术, 2005, 27 (11): 1893- 1894，1974.
	LV Y S, WANG S Z, WANG X W, et al. Research on radar jamming resource allocation strategy based on proximity degree[J]. Systems Engineering and Electronics, 2005, 27 (11): 1893- 1894，1974.
4	任松, 司长哲, 雷军. 雷达干扰机分配的模糊多属性动态规划模型[J]. 系统工程与电子技术, 2008, 30 (10): 1909- 1913.
	REN S, SI C Z, LEI J. Fuzzy multi-attribute dynamic programming model for radar jammer assignment[J]. Systems Engineering and Electronics, 2008, 30 (10): 1909- 1913.
5	薛羽, 庄毅, 张友益, 等. 基于启发式自适应离散差分进化算法的多UCAV协同干扰空战决策[J]. 航空学报, 2013, 34 (2): 343- 351.
	XUE Y, ZHUANG Y, ZHANG Y Y, et al. Multiple UCAV cooperative jamming air combat decision making based on heuristic self-adaptive discrete differential evolution algorithm[J]. Acta Aeronautica et Astronautica Sinica, 2013, 34 (2): 343- 351.
6	LI X, ZHANG S P, SHAO P. Discrete artificial bee colony algorithm with fixed neighborhood search for traveling salesman problem[J]. Engineering Applications of Artificial Intelligence, 2024, 131, 107816. doi: 10.1016/j.engappai.2023.107816
7	KANG Y X, YU H B, KANG L, et al. A multi-role steered artificial bee colony algorithm with variable dimensionality perturbation for multimodal optimization problems[J]. Memetic Computing, 2024, 16, 159- 178. doi: 10.1007/s12293-024-00411-9
8	PHOEMPHON S. Grouping and reflection of the artificial bee colony algorithm for high-dimensional numerical optimization problems[J]. IEEE Access, 2024, 12, 91426- 91446. doi: 10.1109/ACCESS.2024.3417530
9	KARIM F K, SIVAKUMAR N R, ALSHETEWI S, et al. An adaptive threshold-based modified artificial bee colony optimization technique for virtual machine placement in cloud datacenters[J]. IEEE Access, 2024, 12, 94296- 94309. doi: 10.1109/ACCESS.2024.3420173
10	CHEN M. Improved artificial bee colony algorithm based on escaped foraging strategy[J]. Journal of the Chinese Institute of Engineers, 2019, 42 (6): 516- 524. doi: 10.1080/02533839.2019.1611479
11	ZHAI X F, ZHUANG Y. IIGA based algorithm for cooperative jamming resource allocation[C]//Proc. of the Asia Pacific Conference on Postgraduate Research in Microelectronics & Electronics, 2009: 368−371.
12	车飞. 认知对抗中干扰效益评估与智能决策方法研究[D]. 哈尔滨: 哈尔滨工程大学, 2019.
	CHE F. Research on evaluation of interference efficiency and intelligent decision-making in cognitive warfare[D]. Harbin: Harbin Engineering University, 2009.
13	尧泽昆, 王超, 施庆展, 等. 基于改进离散模拟退火遗传算法的雷达网协同干扰资源分配模型[J]. 系统工程与电子技术, 2024, 46 (3): 824- 830.
	YAO Z K, WANG C, SHI Q Z, et al. Cooperative jamming resource allocation model for radar network based on improved discrete simulated annealing genetic algorithm[J]. Systems Engineering and Electronics, 2024, 46 (3): 824- 830.
14	KUYA Y, KOMATSU K, YONAGA K , et al. Quantum annealing-based algorithm for lattice gas automata[J]. Computers & Fluids, 2024, 274: 106238.
15	OSNER N R, PLESSIS W P. Threat evaluation and jamming allocation[J]. IET Radar, Sonar & Navigation, 2017, 11(3): 459−465.
16	呙鹏程, 王星, 周东青, 等. 基于熵权的灰色关联辐射源威胁评估[J]. 空军工程大学学报（自然科学版）, 2016, 17 (6): 58- 63.
	GUO P C, WANG X, ZHOU D Q, et al. Grey correlation emitter threat assessment based on entropy weight[J]. Journal of Air Force Engineering University, 2016, 17 (6): 58- 63.
17	于旺, 石艳, 赵海妮, 等. 改进粒子群算法的集群协同干扰资源分配[J]. 战术导弹技术, 2023 (2): 122- 129.
	YU W, SHI Y, ZHAO H N, et al. Improved particle swarm optimization algorithm for cluster cooperative interference resource allocation[J]. Tactical Missile Technology, 2023 (2): 122- 129.
18	LUO S, WEI Z H, MA Z Y, et al. Multi-loitering munitions cooperative interference resource allocation based on hybrid ant colony algorithm[C]//Proc. of the Chinese Control and Decision Conference, 2021.
19	AHMED A, ZHANG Y D. Optimized resource allocation for distributed joint radar-communication system[J]. IEEE Trans. on Vehicular Technology, 2023, 73 (3): 3872- 3885.
20	HU J, LEI Z, PRAMOD K P, et al. Resource allocation for distributed multi-target tracking in radar networks with missing data[J]. IEEE Trans. on Signal Processing, 2024, 72, 718- 734. doi: 10.1109/TSP.2024.3352915
21	WU Z D, HU S L, LUO Y S, et al. Optimal distributed cooperative jamming resource allocation for multi-missile threat scenario[J]. IET Radar, Sonar & Navigation, 2022, 16(1): 113−128.
22	HAN L, NING Q, CHEN B C, et al. Ground threat evaluation and jamming allocation model with Markov chain for aircraft[J]. IET Radar, Sonar & Navigation, 2020, 14(7): 1039−1045.
23	纪慧颖, 潘明海, 张元时, 等. 基于遗传-蚁群融合算法的干扰资源分配方法[J]. 系统工程与电子技术, 2023, 45 (7): 2098- 2107.
	JI H Y, PAN M H, ZHANG Y S, et al. Interference resource allocation method based on genetic-ant colony fusion algorithm[J]. Systems Engineering and Electronics, 2023, 45 (7): 2098- 2107.
24	ZHANG H W, LIU W J, LIU Y, et al. Joint target and user assignment as well as dwell time and spectrum allocation in a distributed radar-communication coexistence network[J]. IEEE Trans. on Aerospace and Electronic Systems, 2023, 60 (1): 1159- 1175.
25	QI X, XIN Z X, CHEN T. Cooperative jamming resource allocation with joint multi-domain information using evolutionary reinforcement learning[J]. Remote Sensing, 2024, 16 (11): 1955. doi: 10.3390/rs16111955
26	GAO W F, HUANG L L, LUO Y T, et al. Constrained optimization by artificial bee colony framework[J]. IEEE Access, 2018, 6, 73829- 73845. doi: 10.1109/ACCESS.2018.2880814
27	ZHANG W C, LI Y N. A many-objective artificial bee colony algorithm based on adaptive grid[J]. IEEE Access, 2021, 9, 97138- 97151. doi: 10.1109/ACCESS.2021.3093381
28	MA T X, DUAN X, XU Y, et al. Research on fault location in DC distribution network based on adaptive artificial bee colony slime mould algorithm[J]. IEEE Access, 2023, 11, 62630- 62638. doi: 10.1109/ACCESS.2023.3287322
29	HOUGARDY S, ZAISER F, ZHONG X. The approximation ratio of the 2-opt heuristic for the metric traveling salesman problem[J]. Operations Research Letters, 2020, 48 (4): 401- 404. doi: 10.1016/j.orl.2020.05.007
30	KIRAN D N R, DETHE C G. Fuzzy rule selection using artificial bee colony optimisation algorithm[J]. International Journal of Advanced Intelligence Paradigms, 2019, 12 (1/2): 164- 177. doi: 10.1504/IJAIP.2019.096961

算法名称	参数名称	数值
GA	种群数量	100
	淘汰加速指数	2
	交叉概率	0.5
	变异概率	0.1
CA	种群数量	100
CA	被宿主发现概率	0.2
ABC	雇佣蜂数量	50
	观察蜂数量	50
	单个蜜源最大开采次数	200
	最大迭代次数	7000
SA-ABC	起始温度	100
SA-ABC	衰减因子	0.99
TSABC	禁忌表容量	200
ESLABC	经验因子a	0.276
ESLABC	经验因子b	0.294

干扰机序号	雷达序号
干扰机序号	1	2	3	4	5	6	7	8	9	10
1	0	0	0	0	3	0	1	0	1	0
2	0	0	2	0	0	0	0	3	0	3
3	1	0	3	3	0	0	0	0	0	0
4	0	2	0	0	0	1	0	1	0	0

[1]	阎潇, 王青平, 胡卫东, 朱虹宇, 王超. 基于椋鸟迁徙的干扰资源动态分配方法[J]. 系统工程与电子技术, 2025, 47(5): 1385-1394.
[2]	郑宪武, 赵红燕, 田甜, 田金文. SAR典型有源干扰仿真与评估方法研究[J]. 系统工程与电子技术, 2025, 47(12): 4068-4077.
[3]	李瑞, 朱梦韬, 李云杰. 基于逆滤波处理的雷达干扰效果在线评估方法[J]. 系统工程与电子技术, 2023, 45(9): 2706-2717.
[4]	纪慧颖, 潘明海, 张元时, 喻庆豪. 基于遗传-蚁群融合算法的干扰资源分配方法[J]. 系统工程与电子技术, 2023, 45(7): 2098-2107.
[5]	褚凯轩, 常天庆, 孔德鹏, 张雷, 孙皓泽. 基于蜂群算法的坦克阵地部署与火力分配模型[J]. 系统工程与电子技术, 2022, 44(2): 546-556.
[6]	来磊, 吴德伟, 邹鲲, 韩昆, 李海林. 基于多准则交互膜进化算法的UAV三维航迹规划[J]. 系统工程与电子技术, 2021, 43(1): 138-146.
[7]	庞育才, 刘松. 基于改进人工蜂群算法的MIMO雷达稀疏阵列优化[J]. 系统工程与电子技术, 2018, 40(5): 1026-1030.
[8]	钟赟, 姚佩阳, 孙昱, 杨娟. 有人/无人机任务联盟分阶段形成方法[J]. 系统工程与电子技术, 2017, 39(9): 2031-2038.
[9]	王慧颖，王文彬. 基于改进搜索策略的混合蜂群算法[J]. 系统工程与电子技术, 2014, 36(10): 2094-2101.
[10]	李鑫滨, 刘磊, 马锴. 基于离散人工蜂群算法的认知无线电频谱分配[J]. Journal of Systems Engineering and Electronics, 2012, 34(10): 2136-2141.
[11]	毕晓君, 王艳娇. 加速收敛的人工蜂群算法[J]. Journal of Systems Engineering and Electronics, 2011, 33(12): 2755-2761.
[12]	毕晓君, 王艳娇. 用于多峰函数优化的小生境人工蜂群算法[J]. Journal of Systems Engineering and Electronics, 2011, 33(11): 2564-2568.

基于经验自学习人工蜂群算法的干扰资源分配

Interference resource allocation based on empirical self-learning artificial bee colony algorithm

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

图/表 8

参考文献 30

相关文章 12

编辑推荐

Metrics

本文评价