Firefly algorithm versus genetic algorithm as powerful variable selection tools and their effect on different multivariate calibration models in spectroscopy: A comparative study

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Date

01/05/2017

Journal Title

Journal ISSN

Volume Title

Type

Article

Publisher

PERGAMON-ELSEVIER SCIENCE LTD

Series Info

SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY;Volume: 170 Pages: 117-123

Abstract

For the first time, a new variable selection method based on swarm intelligence namely firefly algorithm is coupled with three different multivariate calibration models namely, concentration residual augmented classical least squares, artificial neural network and support vector regression in UV spectral data. A comparative study between the firefly algorithm and the well-known genetic algorithm was developed. The discussion revealed the superiority of using this new powerful algorithm over the well-known genetic algorithm. Moreover, different statistical tests were performed and no significant differences were found between all the models regarding their predictabilities. This ensures that simpler and faster models were obtained without any deterioration of the quality of the calibration. (C) 2016 Elsevier B.V. All rights reserved.

Description

Accession Number: WOS:000398746600016

Keywords

REGRESSION, HYDROCHLORIDE, CHEMOMETRIC MODELS, ARTIFICIAL NEURAL-NETWORK, Support vector regression, Concentration residual augmented classical least squares, Genetic algorithm, Firefly algorithm

Citation