基于混合双链量子遗传算法的干扰效能评估方法

doi:10.12305/j.issn.1001-506X.2025.12.30

摘要/Abstract

摘要：

针对传统效能评估方法无法及时获取评估结果的问题，提出一种混合双链量子遗传算法的在线评估方法。所提算法在量子门更新中采用了一种新的旋转角变化方式，根据目标函数的梯度变化和迭代次数自适应调整角度大小，同时引入量子全干扰交叉和精英个体保留策略，提高了算法的全局搜索能力和收敛速度。仿真结果表明，与其他算法相比，所提算法在不同优化问题的标准测试函数中，性能表现更好。在干扰评估方面，优化后的模型准确率提升约4.5%，能更精确反映干扰效果，且评估结果相较于传统效能评估更具时效性。

关键词: 干扰效能评估, 双链量子遗传算法, 自适应旋转角, 数字通信, 随机森林

Abstract:

To solve the problem that traditional performance evaluation methods cannot obtain evaluation results in a timely manner, a hybrid duble chain quantum genetic algorithm online evaluation method is proposed. The proposed algorithm adopts a new rotation angle change method in quantum gate updates, which adaptively adjusts the angle size based on the gradient change and iteration number of the objective function. At the same time, quantum full interference crossover and elite individual retention strategies are introduced to improve the global search ability and convergence speed of the algorithm. The simulation results show that compared with other algorithms, the proposed algorithm performs better in the standard test functions of different optimization problems. In terms of interference evaluation, the optimized model achieves an accuracy improvement of approximatedly 4.5%, enabling more precise reflection of interference effects. And the evaluation results are greater timeliness compared to traditional performance evaluation.

Key words: interference effectiveness evaluation, double chains quantum genetic algorithm, adaptive rotation angle, digital communications, random Forest

中图分类号:

TN 92

孙志国, 刘传令, 王震铎. 基于混合双链量子遗传算法的干扰效能评估方法[J]. 系统工程与电子技术, 2025, 47(12): 4186-4195.

Zhiguo SUN, Chuanling LIU, Zhenduo WANG. Interference efficiency evaluation method based on hybrid double chain quantum genetic algorithm[J]. Systems Engineering and Electronics, 2025, 47(12): 4186-4195.

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优化算法	参数	数值
QGA	量子变异概率	$ {P_{{\mathrm{QGA}}}} = 0.05 $
QGA	固定旋转角	$ {\theta _{{\mathrm{QGA}}}} = 0.01{\text{π}} $
DCQGA	量子变异概率	$ {P_{{\mathrm{DCQGA}}}} = 0.05 $
DCQGA	初始相位角	$ {\theta _{{\mathrm{DCQGA}}}} = 0.1{\text{π}} $
FDCQGA^[23]	量子变异概率	$ {P_{{\mathrm{FDCQGA}}}} = 0.05 $
FDCQGA^[23]	初始相位角	$ {\theta _0} = 0.1{\text{π}} $
IQGA^[24]	最大权重	$ {w_{\max }} = 0.9 $
IQGA^[24]	最小权重	$ {w_{\min }} = 0.5 $
HDCQGA	量子交叉概率	$ {c_{{\mathrm{HDCQGA}}}} = 0.2 $
	量子变异概率	$ {P_{{\mathrm{HDCQGA}}}} = 0.05 $
	最大相位角	$ {\theta _{\max }} = 0.15{\text{π}} $
	最小相位角	$ {\theta _{\min }} = 0.05{\text{π}} $
	调整因子	$ \lambda = 1 $

算法	F1			F2
算法	均值	方差	最优值	均值	方差	最优值
QGA	0.00658	0.00381	1.53×10⁻⁹	0.00717	1.08×10⁻⁴	1.31×10⁻⁴
DCQGA	0.00224	0.02655	7.73×10⁻⁵	0.04406	0.00543	5.79×10⁻³
FDCQGA	0.00586	0.00382	8.05×10⁻⁶	0.00264	3.16×10⁻⁶	2.17×10⁻⁴
IQGA	0.00161	1.87×10⁻⁵	2.09×10⁻⁵	0.00508	2.23×10⁻⁴	1.74×10⁻⁴
HDCQGA	0.00041	2.09×10⁻⁷	1.60×10^-11	0.00024	1.19×10⁻⁶	6.50×10⁻⁸
算法	F3			F4
算法	均值	方差	最优值	均值	方差	最优值
QGA	0.98913	6.14×10⁻⁶	0.99028	0.02881	0.02672	2.47×10⁻⁵
DCQGA	0.99174	9.93×10⁻⁶	0.99923	0.01387	0.01633	1.33×10⁻⁵
FDCQGA	0.99262	1.07×10⁻⁵	0.99944	0.03742	0.06351	1.78×10⁻⁷
IQGA	0.99439	1.01×10⁻⁵	1.00000	0.00339	0.00153	6.88×10^-10
HDCQGA	0.99772	1.33×10⁻⁵	1.00000	0.00317	7.35×10⁻⁴	1.06×10^-11
算法	F5			F6
算法	均值	方差	最优值	均值	方差	最优值
QGA	−182.500	77.13213	−186.731	−1.00682	0.00290	−1.031612
DCQGA	−183.386	25.46894	−186.561	−1.02088	0.00272	−1.031591
FDCQGA	−184.861	50.16815	−186.723	−1.01295	0.003272	−1.031592
IQGA	−185.005	27.88914	−186.729	−1.02283	1.49×10⁻⁴	−1.031628
HDCQGA	−185.025	55.18085	−186.731	−1.02574	4.50×10⁻⁴	−1.031628

待优化参数	取值范围
决策树数量	[20,120]
叶子节点数量	[1,100]
特征数量	[1,8]
叶子节点包含样本数	[1,100]

算法	50%		70%		90%
算法	最优准确率/ %	最优收敛代数	最优准确率/ %	最优收敛代数	最优准确率/ %	最优收敛代数
QGA	75.69	18	83.49	22	87.97	20
DCQGA	76.42	32	83.70	36	89.05	36
FDCQGA	79.95	33	86.49	26	90.03	37
IQGA	81.42	32	87.01	32	91.00	38
HDCQGA	82.57	29	87.98	33	91.20	32

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