title: Firefly algorithm versus genetic algorithm as powerful variable selection tools and their effect on different multivariate calibration models in spectroscopy: A comparative study.
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.
authors.affiliation: Pharmaceutical Analytical Chemistry Department, Faculty of Pharmacy, Al-Azhar University, 11751 Nasr City, Cairo, Egypt.
authors.forename: Khalid A M
authors.initials: KA
authors.lastname: Attia
authors.affiliation: Pharmaceutical Analytical Chemistry Department, Faculty of Pharmacy, Al-Azhar University, 11751 Nasr City, Cairo, Egypt.
authors.forename: Mohammed W I
authors.initials: MW
authors.lastname: Nassar
authors.affiliation: Analytical Chemistry Department, Faculty of Pharmacy, Modern University for Technology and Information (MTI), 12582 Al Hadaba Al Wosta, Cairo, Egypt.
authors.forename: Mohamed B
authors.initials: MB
authors.lastname: El-Zeiny
authors.affiliation: Pharmaceutical Analytical Chemistry Department, Faculty of Pharmacy, Al-Azhar University, 11751 Nasr City, Cairo, Egypt. Electronic address: Ahmedserag777@hotmail.com.
authors.forename: Ahmed
authors.initials: A
authors.lastname: Serag
keywords: Artificial neural network; Concentration residual augmented classical least squares; Firefly algorithm; Genetic algorithm; Support vector regression