基于改进哈里斯鹰优化算法的光谱特征波段选择模型研究

doi:10.3964/j.issn.1000-0593(2024)01-0148-10

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摘要：特征波段选择是近红外光谱分析的关键步骤之一，有效的特征波段选择能提高建模效率与模型性能。传统的特征波段选择算法存在运行时间长、选择特征冗余的缺陷，在实际工程应用中难以达到期望的效果。哈里斯鹰优化（HHO）算法具有原理简单、参数少的优点，但同时也存在收敛精度低且易陷入局部最优的不足。在HHO算法的基础上提出了一种基于改进哈里斯鹰优化（IHHO）算法的近红外光谱特征波段选择模型。针对HHO算法只能用于求解连续空间的优化问题，采用离散化策略对HHO算法进行修正，使其能求解离散形式的特征波段选择问题；考虑到HHO算法初始种群的质量差，使用混沌映射、反向学习提高初始种群的质量，以增强算法的全局探索能力；由于HHO算法在局部搜索时的收敛精度低，提出了新的猎物能量衰减模型与跳跃策略，以进一步增强算法在局部搜索时的寻优能力；为避免算法在寻优过程中落入局部最优，借鉴了遗传算法的变异方式对HHO算法进行扰动。使用竞争性自适应重加权采样法（CARS）、连续投影算法（SPA）、粒子群优化（PSO）算法、遗传算法（GA）、HHO算法与IHHO算法进行比较，并以4个定性分析近红外光谱数据集与2个定量分析近红外光谱数据集分别建立了支持向量机（SVM）识别模型和偏最小二乘回归（PLSR）模型。在定性分析实验中，IHHO算法得到的平均准确率相对于全波段时分别提高了0.83%、9.55%、17.65%以及0%，平均特征波段数仅占全波段的9.97%、2.59%、1.36%以及0.59%。在定量分析实验中，IHHO算法得到的平均决定系数分别较全波段提高了10.57%、1.47%、4.41%、3.66%以及3.06%，平均均方根误差分别较全波段较低了0.162、1.266 3、1.868、1.869 4以及0.408 4，平均特征波段数仅占全波段的9.24%、10.53%、6.54%、6.91%以及7.14%。实验结果表明，IHHO算法在选择特征波段时能够去冗余，针对性选择最重要的特征波段，其性能均优于比较的几种算法。IHHO算法具有良好的应用前景。

关键词：近红外光谱分析；特征波段选择；哈里斯鹰优化算法；支持向量机；偏最小二乘回归

Abstract：As one of the primary steps in NIR spectral analysis, effective feature band selection can improve modelling efficiency and model performance. Traditional Characteristic band selection algorithms suffer from long run times and redundant feature selection, making achieving the desired results in practical engineering applications difficult. The Harris Hawk Optimisation (HHO) algorithm has the advantages of simple principles and few parameters, but it also has the shortcomings of low convergence accuracy and easy to fall into local optimum. In this paper, we propose an NIR spectral feature band selection model based on the Improved Harris Hawk Optimisation (IHHO) algorithm based on the HHO algorithm. For the HHO algorithm can only be used to solve optimization problems in continuous space, a discretization strategy is used to modify the HHO algorithm so that it can solve the discrete form of the characteristic waveform selection problem. Considering the poor quality of the initial population of the HHO algorithm, the quality of the initial population is improved using chaotic mapping and opposition-based learning to enhance the global exploration capability of the algorithm; Due to the low convergence accuracy of the HHO algorithm in local search, a new prey energy decay model and jumping strategy are proposed further to enhance the algorithm's search capability in local search. The HHO algorithm is perturbed by borrowing the variational approach of genetic algorithm. Support vector machine (SVM) identification models and partial least squares regression (PLSR) models were developed using competitive adaptive reweighted sampling (CARS), successive projections algorithm (SPA), particle swarm optimization (PSO) algorithms, genetic algorithms (GA), HHO algorithms compared to IHHO algorithms, and four qualitative analysis NIR spectral datasets and two quantitative analysis NIR spectral datasets, respectively. In the qualitative analysis experiments, the average accuracy obtained by the IHHO algorithm improved by 0.83%, 9.55%, 17.65%, and 0%, respectively, concerning the full band, and the average number of characteristic bands was only 9.97%, 2.59%, 1.36%, and 0.59% of the full band. In the quantitative analysis experiments, the average coefficient of determination obtained by the IHHO algorithm was 10.57%, 1.47%, 4.41%, 3.66% and 3.06% higher than the full band, and the average root mean square error was 0.162, 1.266 3, 1.868, 1.869 4 and 0.408 4 lower than the full band, and the average number of characteristic bands was only 9.24%, 10.53% and 0% of the full band. The average number of characteristic bands was only 9.24%, 10.53%, 6.54%, 6.91% and 7.14% of the full band. The experimental results show that the IHHO algorithm can remove redundancy in the selection of feature bands and target the most important ones, and its performance is better than several other selection algorithms. Therefore, the IHHO algorithm has good application prospects.

Key words：Near infrared spectroscopy; Feature band selection; Harris hawk optimization algorithm; Support vector machine; Partial least squares regression

收稿日期: 2022-06-02 修订日期: 2022-11-21

中图分类号:

O657.33

基金资助: 国家自然科学基金项目（62141501，62265003），贵州省科技厅学术新苗培养及创新探索专项项目（GYU-KJT[2022]）资助

通讯作者: 张艳 E-mail: Eileen_zy001@sohu.com

作者简介: 鲍浩，1998年生，贵州大学大数据与信息工程学院硕士研究生 e-mail: 26620519979@qq.com

引用本文:

鲍浩，张艳. 基于改进哈里斯鹰优化算法的光谱特征波段选择模型研究[J]. 光谱学与光谱分析, 2024, 44(01): 148-157.
BAO Hao，ZHANG Yan. Research on Spectral Feature Band Selection Model Based on Improved Harris Hawk Optimization Algorithm. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 148-157.

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