Study on bionics-based swarm intelligence optimization algorithms for wavelength selection in near-infrared spectroscopy - Long, Tingze; Yi, Han; Kang, Yatong; Qiao, Ying; Guan, Ying; Chen, Chao (Infrared physics & technology, 2024)
In this paper, ten bionic swarm intelligence optimization algorithms (BSIOAs) inspired by natural organisms, such as Harris Hawk Optimization Algorithm, Butterfly Optimization Algorithm, Whale Optimization Algorithm, etc, were studied. The results evaluated with three benchmark datasets show that these BSIOAs not only significantly reduce the number of wavelengths (retaining less than half or even fewer wavelengths), but also improve the model performance. In addition, their performance is generally better than some popular classical wavelength selection algorithms. [Display omitted] •Ten BSIOAs are investigated for wavelength selection in NIRS modeling.•Three benchmark NIRS datasets are employed for performance evaluation.•BSIOAs not only simplify the model complexity but improve the model quality.•BSIOAs generally perform better than the classic variable selection methods. Wavelength selection is one of the most important steps in the modeling of near-infrared spectroscopy (NIRS), which is of great significance to reduce model complexity and improve model performance. In this paper, a total of ten bionics-based swarm intelligence optimization algorithms (BSIOAs) inspired by natural creatures, such as Harris Hawks Optimization (HHO), Butterfly Optimization Algorithm (BOA), Whale Optimization Algorithm (WOA), Monarch Butterfly Optimization (MBO), Grey Wolf Optimization (GWO), Fruit Fly Optimization Algorithm (FOA), Bat Algorithm (BA), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA) were studied on application to wavelength selection in the NIRS modeling. Three benchmark NIRS datasets were used to evaluate the algorithms by calculating the indicators, including coefficients of determination, root mean square error, and residual predictive deviation in calibration and prediction. The results obtained showed that these BSIOAs can significantly reduce the number of wavelengths (retaining half or fewer). Compared with the full-spectrum models, the present models not only simplified the model structures but improved the model performances. The performances were generally better than the ones by some popular and classic wavelength selection algorithms, such as competitive adaptive reweighted sampling, Monte Carlo uninformative variable elimination, variable importance in projection, interval partial least-squares, and successive projections algorithm.