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

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...

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Bibliographic Details
Published in:PloS one 2014-12, Vol.9 (12), p.e114145-e114145
Main Authors: de Paula, Lauro C M, Soares, Anderson S, de Lima, Telma W, Delbem, Alexandre C B, Coelho, Clarimar J, Filho, Arlindo R G
Format: Article
Language:English
Subjects:
Algorithms
Analysis
Biology and Life Sciences
CAD
Calibration
Computer aided design
Computer and Information Sciences
Computer Graphics
Design of experiments
Engineering and Technology
Fireflies
Graphics processing units
Heuristic methods
Informatics
Laboratories
Methods
Multiple objective analysis
Multivariate Analysis
Physical Sciences
Problems
Research and Analysis Methods
Software
Variables
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Summary: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.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0114145