基于多适应度量子遗传算法的X射线荧光重叠峰分解

智能算法在对谱峰重叠严重的复杂地质样品进行分析时，往往存在计算量过大、弱峰误差较大、收敛于局部极小值或不收敛等问题。量子遗传算法因其具有良好的收敛性，可用于X射线荧光光谱重叠峰的分解。针对X射线荧光分析过程中经常遇到的谱峰重叠问题，提出了一种基于元素关联高斯混合模型（GMM-ER）和多适应度量子遗传算法的重叠峰分解方法。首先介绍了基于元素K系和L系特征X射线的重叠峰GMM-EB模型。然后基于X射线荧光光谱的物理特性，对传统量子遗传算法进行了改进，引入了多适应度函数。由锰、铁、钴和镍的特征X射线产生一段谱峰严重重叠的模拟光谱，然后基于GMM-EB模型，分别用传统量子遗传算法和改进的多适应度量子遗传算法对模拟光谱进行了10次解析。实验结果显示，改进后的量子遗传算法的重叠峰分解精度平均提高了32.1%，最佳分解精度提高了73.9%。应用改进量子遗传算法进行分解时，含量比例低的元素分解精度得到较大改善，最佳情况下元素分解的相对误差范围缩小了64.5%。并且，改进算法收敛速度快于传统算法。该方法适合严重重叠谱峰的分解，且对弱峰有较高的分解精度。

Decomposition of X-Ray Fluorescence Overlapping Peaks Based on Quantum Genetic Algorithm With Multi-Fitness Function

When the intelligent algorithm is used to analyze the complex geological samples with serious overlapping spectral peaks,there are some problems such as big calculation,large error of weak peaks,convergence to local minimum or non-convergence.Because of its good convergence,the quantum genetic algorithm can decompose overlapping peaks in X-ray fluorescence spectra.A method of overlapping peak decomposition based on the GMM-ER model and quantum genetic algorithm with multi-fitness function is proposed.The overlapping peak model(GMM-ER)is first introduced based on K-series and L-Series of element characteristic X-ray.Then,based on the physical characteristics of the X-ray fluorescence spectrum,a multi-fitness function is introduced into the traditional quantum genetic algorithm.The simulated spectra are generated by the characteristic X-rays of Mn,Fe,Co and Ni.Then,based on the GMM-ER model,the simulated spectra are analyzed 10 times by traditional quantum genetic algorithm and improved multi-fitness quantum genetic algorithm,respectively.The experimental results show that the average decomposition accuracy of overlapping peaks is improved by 32.1%,and the optimal decomposition accuracy is improved by 73.9%.Using the improved algorithm,the decomposition accuracy of elements with a low content ratio is greatly improved,and the relative error range of element decomposition is reduced by 64.5%under the optimal decomposition accuracy.Moreover,the convergence speed of the improved algorithm is faster than that of the traditional algorithm.This method is suitable for the decomposition of seriously overlapped peaks and has a high resolution for weak peaks.