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GPU-Based Approaches for Multiobjective Local Search Algorithms. A Case Study: The Flowshop Scheduling Problem

  • Thé Van Luong
  • Nouredine Melab
  • El-Ghazali Talbi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6622)

Abstract

Multiobjective local search algorithms are efficient methods to solve complex problems in science and industry. Even if these heuristics allow to significantly reduce the computational time of the solution search space exploration, this latter cost remains exorbitant when very large problem instances are to be solved. As a result, the use of graphics processing units (GPU) has been recently revealed as an efficient way to accelerate the search process. This paper presents a new methodology to design and implement efficiently GPU-based multiobjective local search algorithms. The experimental results show that the approach is promising especially for large problem instances.

Keywords

Graphic Process Unit Global Memory Memory Management Thread Block Flowshop Schedule Problem 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Thé Van Luong
    • 1
  • Nouredine Melab
    • 1
  • El-Ghazali Talbi
    • 1
  1. 1.INRIA Dolphin Project / Opac LIFL CNRSVilleneuve d’Ascq CedexFrance

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