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Journal Articles Journal of Parallel and Distributed Computing Year : 2006

Grid computing for parallel bioinspired algorithms

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.

Domains

Other [cs.OH]

Dates and versions

hal-00684951 , version 1 (03-04-2012)

Identifiers

Cite

Nouredine Melab, S. Cahon, El-Ghazali Talbi. Grid computing for parallel bioinspired algorithms. Journal of Parallel and Distributed Computing, 2006, 66 (8), pp.1052-1061. ⟨10.1016/j.jpdc.2005.11.006⟩. ⟨hal-00684951⟩
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