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P2P B&B and GA for the flow-shop scheduling problem

Abstract : Solving exactly Combinatorial Optimization Problems (COPs) using a Branch-and-Bound algorithm (B&B) requires a huge amount of computational resources. The efficiency of such algorithm can be improved by its hybridization with meta-heuristics such as Genetic Algorithms (GA) which proved their effectiveness, since they generate acceptable solutions in a reasonable time. Moreover, distributing at large scale the computation, using for instance Peer-to-Peer (P2P) Computing, provides an efficient way to reach high computing performance. In this chapter, we propose ParallelBB and ParallelGA, which are P2P-based parallelization of the B&B and GA algorithms for the computational Grid. The two algorithms have been implemented using the ProActive distributed object Grid middleware. The algorithms have been applied to a mono-criterion permutation flow-shop scheduling problem and promisingly experimented on the Grid5000 computational Grid.
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Submitted on : Monday, April 23, 2012 - 11:39:26 AM
Last modification on : Thursday, January 20, 2022 - 5:27:53 PM

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Ahcène Bendjoudi, Samir Guerdah, Madjid Mansoura, Nouredine Melab, El-Ghazali Talbi. P2P B&B and GA for the flow-shop scheduling problem. Springer. Metaheuristics for Scheduling in Distributed Computing Environments, 146, Springer, pp.301-321, 2008, Studies in Computational Intelligence, ⟨10.1007/978-3-540-69277-5_11⟩. ⟨hal-00690360⟩



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