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Parallelization of a Modified Firefly Algorithm using GPU for Variable Selection in a Multivariate Calibration Problem

Parallelization of a Modified Firefly Algorithm using GPU for Variable Selection in a Multivariate Calibration Problem

Lauro C. M. de Paula, Anderson S. Soares, Telma W. L. Soares, Alexandre C. B. Delbem, Clarimar J. Coelho, Arlindo R. G. Filho
Copyright: © 2014 |Volume: 4 |Issue: 1 |Pages: 12
ISSN: 1947-928X|EISSN: 1947-9298|EISBN13: 9781466655812|DOI: 10.4018/ijncr.2014010103
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MLA

de Paula, Lauro C. M., et al. "Parallelization of a Modified Firefly Algorithm using GPU for Variable Selection in a Multivariate Calibration Problem." IJNCR vol.4, no.1 2014: pp.31-42. http://doi.org/10.4018/ijncr.2014010103

APA

de Paula, L. C., Soares, A. S., Soares, T. W., Delbem, A. C., Coelho, C. J., & Filho, A. R. (2014). Parallelization of a Modified Firefly Algorithm using GPU for Variable Selection in a Multivariate Calibration Problem. International Journal of Natural Computing Research (IJNCR), 4(1), 31-42. http://doi.org/10.4018/ijncr.2014010103

Chicago

de Paula, Lauro C. M., et al. "Parallelization of a Modified Firefly Algorithm using GPU for Variable Selection in a Multivariate Calibration Problem," International Journal of Natural Computing Research (IJNCR) 4, no.1: 31-42. http://doi.org/10.4018/ijncr.2014010103

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Abstract

The recent improvements of Graphics Processing Units (GPU) have provided to the bio-inspired algorithms a powerful processing platform. Indeed, a lot of highly parallelizable problems can be significantly accelerated using GPU architecture. Among these algorithms, the Firefly Algorithm (FA) is a newly proposed method with potential application in several real world problems such as variable selection problem in multivariate calibration. The main drawback of this task lies in its computation burden, as it grows polynomially with the number of variables available. In this context, this paper proposes a GPU-based FA for variable selection in a multivariate calibration problem. Such implementation is aimed at improving the computational efficiency of the algorithm. For this purpose, a new strategy of regression coefficients calculation is employed. The advantage of the proposed implementation is demonstrated in an example involving a large number of variables. In such example, gains of speedup were obtained. Additionally the authors also demonstrate that the FA, in comparison with traditional algorithms, can be a relevant contribution for the variable selection problem.

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