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GPU-Based Hybrid Cellular Genetic Algorithm for Job-Shop Scheduling Problem

GPU-Based Hybrid Cellular Genetic Algorithm for Job-Shop Scheduling Problem

Abdelkader Amrane, Fatima Debbat, Khadidja Yahyaoui
Copyright: © 2021 |Volume: 12 |Issue: 2 |Pages: 15
ISSN: 1947-8283|EISSN: 1947-8291|EISBN13: 9781799861133|DOI: 10.4018/IJAMC.2021040101
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MLA

Amrane, Abdelkader, et al. "GPU-Based Hybrid Cellular Genetic Algorithm for Job-Shop Scheduling Problem." IJAMC vol.12, no.2 2021: pp.1-15. http://doi.org/10.4018/IJAMC.2021040101

APA

Amrane, A., Debbat, F., & Yahyaoui, K. (2021). GPU-Based Hybrid Cellular Genetic Algorithm for Job-Shop Scheduling Problem. International Journal of Applied Metaheuristic Computing (IJAMC), 12(2), 1-15. http://doi.org/10.4018/IJAMC.2021040101

Chicago

Amrane, Abdelkader, Fatima Debbat, and Khadidja Yahyaoui. "GPU-Based Hybrid Cellular Genetic Algorithm for Job-Shop Scheduling Problem," International Journal of Applied Metaheuristic Computing (IJAMC) 12, no.2: 1-15. http://doi.org/10.4018/IJAMC.2021040101

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Abstract

In task scheduling, the job-shop scheduling problem is notorious for being a combinatorial optimization problem; it is considered among the largest class of NP-hard problems. In this paper, a parallel implementation of hybrid cellular genetic algorithm is proposed in order to reach the best solutions at a minimum execution time. To avoid additional computation time and for real-time control, the fitness evaluation and genetic operations are entirely executed on a graphic processing unit in parallel; moreover, the chosen genetic representation, as well as the crossover, will always give a feasible solution. In this paper, a two-level scheme is proposed; the first and fastest uses several subpopulations in the same block, and the best solutions migrate between subpopulations. To achieve the optimal performance of the device and to reshape a more complex problem, a projection of the first on different blocks will make the second level. The proposed solution leads to speedups 18 times higher when compared to the best-performing algorithms.

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