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The Design and Implementation of a GPU-enabled Multi-objective Tabu-search Intended for Real World and High-dimensional Applications

Christos Tsotskas, Timoleon Kipouros, Anthony Mark Savill
Department of Power and Propulsion Cranfield University, Bedfordshire, U.K.
Procedia Computer Science, Volume 29, Pages 2152-2161, 2014

@article{Tsotskas20142152,

   title={The Design and Implementation of a GPU-enabled Multi-objective Tabu-search Intended for Real World and High-dimensional Applications},

   journal={Procedia Computer Science},

   volume={29},

   number={0},

   pages={2152 – 2161},

   year={2014},

   note={2014 International Conference on Computational Science},

   issn={1877-0509},

   doi={http://dx.doi.org/10.1016/j.procs.2014.05.200},

   url={http://www.sciencedirect.com/science/article/pii/S1877050914003779},

   author={Christos Tsotskas and Timoleon Kipouros and Anthony Mark Savill}

}

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Metaheuristics is a class of approximate methods based on heuristics that can effectively handle real world (usually NP-hard) problems of high-dimensionality with multiple objectives. An existing multi-objective Tabu-Search (MOTS2) has been re-designed by and ported onto Compute Unified Device Architecture (CUDA) so as to effectively deal with a scalable multi-objective problem with a range of decision variables. The high computational cost due to the problem complexity is addressed by employing Graphics Processing Units (GPUs), which alleviate the computational intensity. The main challenges of the re-implementation are the effective communication with the GPU and the transparent integration with the optimization procedures. Finally, future work is proposed towards heterogeneous applications, where improved features are accelerated by the GPUs.
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