Abstract
This work proposes a GPU-based parallelization for the Continuous GRASP (C-GRASP), a local search metaheuristic for finding cost-efficient solutions to continuous global optimization problems subject to box constraints. C-GRASP has demonstrated competitive performance on several well-known multimodal test functions as well as on difficult real-world problems, hence a GPU parallelization might increase even further the applicability of this metaheuristic. Although GPU parallelizations have been proposed for many metaheuristics, little has been done for C-GRASP. We conduct an extensive set of experiments and compare our proposal with state-of-the-art GPU parallelizations of other metaheuristics, such as Scatter Search and Differential Evolution. We also compare our GPU approach with two other C-GRASP implementations: a sequential version and a multi-core version. Experimental results show our GPU C-GRASP outperforms other GPU-based metaheuristics and the multi-core C-GRASP. Besides, we observed speedups of up to \(154{\times }\) over the sequential version.
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Notes
The source code of our implementation can be downloaded at https://sites.google.com/site/nogueirabruno/software.
Functions in which the sequential C-GRASP finds an optimal solution in less than 1 ms were omitted from this table.
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Nogueira, B., Tavares, E., Araujo, J. et al. Accelerating continuous GRASP with a GPU. J Supercomput 75, 5741–5759 (2019). https://doi.org/10.1007/s11227-019-02833-6
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DOI: https://doi.org/10.1007/s11227-019-02833-6