Ant colony optimisation: a powerful tool for wavelength selection

Ant colony optimisation (ACO) is a meta-heuristic algorithm, which is derived from the observation of real ants. Real ant colonies are distributed system that, in spite of the simplicity of their individuals, present a highly structured social organisation and can accomplish complex tasks. They always find a short path between the nest and a food source. ACO is based on local message exchange via the deposition of pheromone trails. It is in fact a population-based approach using positive feedback as well as greedy search. Wavelength selection is a strategy used for improving the quality of calibration methods. As a first report, this work indicated that the ACO possesses a great ability to find best subsets of wavelengths, at a short period of time with small PRESS values, via accumulation of information in the form of pheromone trails deposited on each wavelength. Theory of ACO is described and, to carry out the wavelength selection, a fitness function is defined. The ACO parameters are configured with a 3-levels full factorial design. The high ability of ACO in wavelength selection process was demonstrated by examining four different NIR and UV/Vis data sets via various ACO algorithms, including ACO-ILS, ACO-CLS and ACO-PLS. The results showed that, with the same fitness function, ACO-ILS algorithm suffers from some overfitting problem. This problem was overcome by constraining the algorithm to choose limited number of wavelengths, the corresponding algorithm called as ACO-ILS(limited). The results obtained by these algorithms clearly revealed the improved predictive ability of ACO in wavelength selection over the existing full-spectrum models. Copyright (c) 2007 John Wiley & Sons, Ltd.