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

Abstract

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

CLC Number:

TN 974

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.

Figures/Tables 8

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References 30

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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

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

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