Analytical Chemistry Department, Faculty of Pharmacy, Modern University for Technology and Information (MTI), Egypt.
Faculty of Computers and Information, Beni-Suef University, Egypt; Faculty of Mathematics and Computer Science, Babes-Bolyai University, Romania.
Spectrochim Acta A Mol Biomol Spectrosc. 2021 Feb 5;246:119042. doi: 10.1016/j.saa.2020.119042. Epub 2020 Oct 6.
Herein, two new swarm intelligence based algorithms namely; grey wolf optimization (GWO) and antlion optimization (ALO) algorithms were presented, for the first time, as variable selection tools in spectroscopic data analysis. In order to assess the performance of these algorithms, they were applied along with the recently introduced firefly algorithm (FFA) and the well-established genetic algorithm (GA) and particle swarm optimization (PSO) algorithm on four different spectroscopic datasets of varying sizes and nature (UV and IR). Partial least squares (PLS) regression models were built using the selected variables by these algorithms along with the full spectral data as the reference models. The obtained results prove that the ALO and GWO optimization algorithms select variables in most cases less than GA and PSO while keeping the PLS performance almost the same. Accordingly, these algorithms can be successfully used for variable selection in spectroscopic data analysis.
本文首次提出了两种新的基于群体智能的算法，即灰狼优化（GWO）算法和蚁狮优化（ALO）算法，作为光谱数据分析中的变量选择工具。为了评估这些算法的性能，将它们与最近提出的萤火虫算法（FFA）以及成熟的遗传算法（GA）和粒子群优化（PSO）算法一起应用于四个不同大小和性质的光谱数据集（UV 和 IR）。使用这些算法选择的变量以及全谱数据构建偏最小二乘（PLS）回归模型作为参考模型。所得结果证明，ALO 和 GWO 优化算法在大多数情况下选择的变量少于 GA 和 PSO，同时保持 PLS 性能几乎相同。因此，这些算法可以成功地用于光谱数据分析中的变量选择。
