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Acceleration of a CUDA-Based Hybrid Genetic Algorithm and its Application to a Flexible Flow Shop Scheduling Problem

Didier El Baz 1 Jia Luo 1 Jinglu Hu 2
1 LAAS-CDA - Équipe Calcul Distribué et Asynchronisme
LAAS - Laboratoire d'analyse et d'architecture des systèmes
Abstract : Genetic Algorithms are commonly used to generate high-quality solutions to combinational optimization problems. However, the execution time can become a limiting factor for large and complex problems. In this paper, we propose a parallel Genetic Algorithm consisting of an island model at the upper level and a fine-grained model at the lower level. It is designed to be highly consistent with the CUDA framework to get the maximum speedup without compromising to solutions' quality. As several parameters control the performance of the hybrid method, we test them by a flexible flow shop scheduling problem and analyze their influence. Finally, numerical experiments show that our approach cannot only obtain competitive results but also reduces execution time by setting a medium size selection diameter, a relatively large island size and a wide range size migration interval.
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https://hal.laas.fr/hal-02091695
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Submitted on : Saturday, April 6, 2019 - 9:03:06 AM
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Didier El Baz, Jia Luo, Jinglu Hu. Acceleration of a CUDA-Based Hybrid Genetic Algorithm and its Application to a Flexible Flow Shop Scheduling Problem. 19th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD 2018), Jun 2018, Busan, South Korea. pp.117-122, ⟨10.1109/SNPD.2018.8441112⟩. ⟨hal-02091695⟩

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