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 and Arlindo R. G. Filho
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Lauro C. M. de Paula: Instituto de Informática, Universidade Federal de Goiás, Goiânia, Goiás, Brazil
Anderson S. Soares: Instituto de Informática, Universidade Federal de Goiás, Goiânia, Goiás, Brazil
Telma W. L. Soares: Instituto de Informática, Universidade Federal de Goiás, Goiânia, Goiás, Brazil
Alexandre C. B. Delbem: Instituto de Ciências Matemáticas e de Computação, Universidade de São Paulo, São Carlos, São Paulo, Brazil
Clarimar J. Coelho: Departamento de Computação, Pontifícia Universidade Católica de Goiás, Goiânia, Goiás, Brazil
Arlindo R. G. Filho: Departamento de Sistemas e Controle, Instituto Tecnológico de Aeronáutica, São José dos Campos, São Paulo, Brazil

International Journal of Natural Computing Research (IJNCR), 2014, vol. 4, issue 1, 31-42

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.

Date: 2014
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