Selection of Near Infrared Spectral Wavelength Variables Based on Improved Whale Optimization Algorithm and Its Application

WANG Zhong-yu; GAO Mei-feng

doi:10.19969/j.fxcsxb.22081803

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Selection of Near Infrared Spectral Wavelength Variables Based on Improved Whale Optimization Algorithm and Its Application

Scientific Papers | 更新时间：2023-05-25

- Selection of Near Infrared Spectral Wavelength Variables Based on Improved Whale Optimization Algorithm and Its Application
- Journal of Instrumental Analysis Vol. 42, Issue 1, Pages: 37-44(2023)
- 作者机构：
  
  江南大学轻工过程先进控制教育部重点实验室，物联网工程学院，江苏无锡 214122
- 作者简介：
- 基金信息：
- DOI：10.19969/j.fxcsxb.22081803
  CLC：
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王仲雨,高美凤.基于改进鲸鱼优化算法的近红外光谱波长变量选择方法及其应用[J].分析测试学报,2023,42(01):37-44.

WANG Zhong-yu,GAO Mei-feng.Selection of Near Infrared Spectral Wavelength Variables Based on Improved Whale Optimization Algorithm and Its Application[J].Journal of Instrumental Analysis,2023,42(01):37-44.
王仲雨,高美凤.基于改进鲸鱼优化算法的近红外光谱波长变量选择方法及其应用[J].分析测试学报,2023,42(01):37-44. DOI： 10.19969/j.fxcsxb.22081803.

WANG Zhong-yu,GAO Mei-feng.Selection of Near Infrared Spectral Wavelength Variables Based on Improved Whale Optimization Algorithm and Its Application[J].Journal of Instrumental Analysis,2023,42(01):37-44. DOI： 10.19969/j.fxcsxb.22081803.

摘要

该文在群体智能的鲸鱼优化算法（WOA）基础上，提出了一种改进的鲸鱼优化算法（iWOA）用于近红外光谱波长的选择。首先引入混沌策略初始化种群，避免算法过早陷入局部最优；其次引入一种非线性时变Sigmoid传递函数和贪心算法思想，提升算法探优能力，使得模型获得更好的预测精度。为验证算法的有效性，以玉米脂肪、蛋白质、淀粉、水4个指标的近红外光谱数据进行偏最小二乘（PLS）建模分析，并与其他算法进行对比。结果表明，iWOA算法能在最短时间内，有效地筛选出波长变量，降低模型的复杂度，提升模型的预测精度。在玉米脂肪、蛋白质、淀粉、水含量的预测上，与全光谱相比，模型的预测集均方根误差（RMSEP）分别从0.077 2、0.122 4、0.334 4、0.059 5降至0.033 2、0.050 7、0.139 2、0.004 4，预测精度分别提升了57.0%、58.6%、58.3%、92.6%；算法选出的波长数目分别为：84、69、87、66。

Abstract

Based on the whale optimization algorithm（WOA） of swarm intelligence，an improved whale optimization algorithm（iWOA） for the selection of near-infrared spectral wavelengths was proposed. Firstly，a chaotic strategy was introduced to initialize the population to avoid the algorithm from falling into local optimization prematurely. Secondly，a nonlinear time-varying Sigmoid transfer function and greedy algorithm were introduced to improve the algorithm's optimization ability and make the model obtain better prediction accuracy. In order to verify the effectiveness of the algorithm，the near-infrared spectral data of four indicators for corn fat，protein，starch and water were used for PLS modeling and analysis，and compared with other algorithms. The results showed that the iWOA algorithm could effectively filter out the wavelength variable in the shortest possible time，reduce the complexity of the model and improve the prediction accuracy of the model. The root mean square errors of prediction（RMSEPs） of the model decreased from 0.077 2，0.122 4，0.334 4 and 0.059 5 to 0.033 2，0.050 7，0.139 2 and 0.004 4，and the prediction accuracy was improved by 57.0%，58.6%，58.3% and 92.6%，respectively，compared with those of the full spectrum. The numbers of wavelengths selected by the algorithm were 84，69，87 and 66，respectively.

关键词

近红外光谱波长选择改进鲸鱼优化算法传递函数贪心算法思想

Keywords

near-infrared spectroscopywavelength selectionimproved whale optimization algorithmtransfer functiongreedy thinking

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