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Parallel Hybrid Evolutionary Algorithms on GPU

Thé Van Luong 1 Nouredine Melab 1 El-Ghazali Talbi 1
1 DOLPHIN - Parallel Cooperative Multi-criteria Optimization
Inria Lille - Nord Europe, LIFL - Laboratoire d'Informatique Fondamentale de Lille
Abstract : Over the last years, interest in hybrid metaheuristics has risen considerably in the field of optimization. Combinations of methods such as evolutionary algorithms and local searches have provided very powerful search algorithms. However, due to their complexity, the computational time of the solution search exploration remains exorbitant when large problem instances are to be solved. Therefore, the use of GPU-based parallel computing is required as a complementary way to speed up the search. This paper presents a new methodology to design and implement efficiently and effectively hybrid evolutionary algorithms on GPU accelerators. The methodology enables efficient mappings of the explored search space onto the GPU memory hierarchy. The experimental results show that the approach is very efficient especially for large problem instances.
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Submitted on : Thursday, September 23, 2010 - 12:44:54 PM
Last modification on : Thursday, January 20, 2022 - 5:27:54 PM
Long-term archiving on: : Friday, December 24, 2010 - 2:47:48 AM


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  • HAL Id : inria-00520466, version 1


Thé Van Luong, Nouredine Melab, El-Ghazali Talbi. Parallel Hybrid Evolutionary Algorithms on GPU. IEEE Congress on Evolutionary Computation (CEC), 2010, Barcelone, Spain. ⟨inria-00520466⟩



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