Grid computing for parallel bioinspired algorithms

Nouredine Melab 1 S. Cahon 2 E.-G. Talbi 1, 2
1 DOLPHIN - Parallel Cooperative Multi-criteria Optimization
LIFL - Laboratoire d'Informatique Fondamentale de Lille, Inria Lille - Nord Europe
Abstract : This paper focuses on solving large size combinatorial optimization problems using a Grid-enabled framework called ParadisEO-CMW (View the MathML sourcellel and View the MathML sourcetributed View the MathML source on top on View the MathML sourceondor and the View the MathML sourceaster View the MathML sourceorker Framework). The latter is an extension of ParadisEO, an open source framework originally intended to the design and deployment of parallel hybrid meta-heuristics on dedicated clusters and networks of workstations. Relying on the Condor-MW framework, it enables the execution of these applications on volatile heterogeneous computational pools of resources. The motivations, architecture and main features will be discussed. The framework has been experimented on a real-world problem: feature selection in near-infrared spectroscopic data mining. It has been solved by deploying a multi-level parallel model of evolutionary algorithms. Experimentations have been carried out on more than 100 PCs originally intended for education. The obtained results are convincing, both in terms of flexibility and easiness at implementation, and in terms of efficiency, quality and robustness of the provided solutions at run time.
Type de document :
Article dans une revue
Journal of Parallel and Distributed Computing, Elsevier, 2006, 66 (8), pp.1052-1061. 〈10.1016/j.jpdc.2005.11.006〉
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Soumis le : mardi 3 avril 2012 - 15:33:11
Dernière modification le : jeudi 11 janvier 2018 - 06:22:13

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Nouredine Melab, S. Cahon, E.-G. Talbi. Grid computing for parallel bioinspired algorithms. Journal of Parallel and Distributed Computing, Elsevier, 2006, 66 (8), pp.1052-1061. 〈10.1016/j.jpdc.2005.11.006〉. 〈hal-00684951〉

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