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A GPU-Based Implementation of the Firefly Algorithm for Variable Selection in Multivariate Calibration Problems

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Listed:
  • Lauro C M de Paula
  • Anderson S Soares
  • Telma W de Lima
  • Alexandre C B Delbem
  • Clarimar J Coelho
  • Arlindo R G Filho

Abstract

Several variable selection algorithms in multivariate calibration can be accelerated using Graphics Processing Units (GPU). Among these algorithms, the Firefly Algorithm (FA) is a recent proposed metaheuristic that may be used for variable selection. This paper presents a GPU-based FA (FA-MLR) with multiobjective formulation for variable selection in multivariate calibration problems and compares it with some traditional sequential algorithms in the literature. The advantage of the proposed implementation is demonstrated in an example involving a relatively large number of variables. The results showed that the FA-MLR, in comparison with the traditional algorithms is a more suitable choice and a relevant contribution for the variable selection problem. Additionally, the results also demonstrated that the FA-MLR performed in a GPU can be five times faster than its sequential implementation.

Suggested Citation

  • Lauro C M de Paula & Anderson S Soares & Telma W de Lima & Alexandre C B Delbem & Clarimar J Coelho & Arlindo R G Filho, 2014. "A GPU-Based Implementation of the Firefly Algorithm for Variable Selection in Multivariate Calibration Problems," PLOS ONE, Public Library of Science, vol. 9(12), pages 1-22, December.
    • Handle: RePEc:plo:pone00:0114145
    DOI: 10.1371/journal.pone.0114145
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    References listed on IDEAS

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    1. Afnizanfaizal Abdullah & Safaai Deris & Sohail Anwar & Satya N V Arjunan, 2013. "An Evolutionary Firefly Algorithm for the Estimation of Nonlinear Biological Model Parameters," PLOS ONE, Public Library of Science, vol. 8(3), pages 1-16, March.
    2. Daniel Vitor de Lucena & Telma Woerle de Lima & Anderson da Silva Soares & Clarimar José Coelho, 2012. "Multi-Objective Evolutionary Algorithm NSGA-II for Variables Selection in Multivariate Calibration Problems," International Journal of Natural Computing Research (IJNCR), IGI Global, vol. 3(4), pages 43-58, October.
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