GPU-based Multi-start Local Search Algorithms
Document type :
Communication dans un congrès avec actes
Title :
GPU-based Multi-start Local Search Algorithms
Author(s) :
Luong, Thé Van [Auteur]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Melab, Nouredine [Auteur]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Talbi, El-Ghazali [Auteur]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Melab, Nouredine [Auteur]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Talbi, El-Ghazali [Auteur]

Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Scientific editor(s) :
Carlos A. Coello Coello
Conference title :
Learning and Intelligent Optimization
City :
Rome
Country :
Italie
Start date of the conference :
2011
Journal title :
Lecture Notes in Computer Science
Publisher :
Springer
Publication date :
2011
HAL domain(s) :
Sciences cognitives/Informatique
English abstract : [en]
In practice, combinatorial optimization problems are complex and computationally time-intensive. Local search algorithms are powerful heuristics which allow to significantly reduce the computation time cost of the solution ...
Show more >In practice, combinatorial optimization problems are complex and computationally time-intensive. Local search algorithms are powerful heuristics which allow to significantly reduce the computation time cost of the solution exploration space. In these algorithms, the multi-start model may improve the quality and the robustness of the obtained solutions. However, solving large size and time-intensive optimization problems with this model requires a large amount of computational resources. GPU computing is recently revealed as a powerful way to harness these resources. In this paper, the focus is on the multi-start model for local search algorithms on GPU. We address its re-design, implementation and associated issues related to the GPU execution context. The preliminary results demonstrate the effectiveness of the proposed approaches and their capabilities to exploit the GPU architecture.Show less >
Show more >In practice, combinatorial optimization problems are complex and computationally time-intensive. Local search algorithms are powerful heuristics which allow to significantly reduce the computation time cost of the solution exploration space. In these algorithms, the multi-start model may improve the quality and the robustness of the obtained solutions. However, solving large size and time-intensive optimization problems with this model requires a large amount of computational resources. GPU computing is recently revealed as a powerful way to harness these resources. In this paper, the focus is on the multi-start model for local search algorithms on GPU. We address its re-design, implementation and associated issues related to the GPU execution context. The preliminary results demonstrate the effectiveness of the proposed approaches and their capabilities to exploit the GPU architecture.Show less >
Language :
Anglais
Peer reviewed article :
Oui
Audience :
Internationale
Popular science :
Non
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