Acceleration of a CUDA-based hybrid genetic algorithm and its application to a flexible flow shop scheduling problem

Jia Luo, Didier El Baz, Jinglu Hu

研究成果: Conference contribution

抜粋

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. This design is highly consistent with the CUDA framework in order 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.

元の言語English
ホスト出版物のタイトルProceedings - 2018 IEEE/ACIS 19th International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, SNPD 2018
編集者Ha Jin Hwang, Lizhi Cai, Gun Huck Yeom, Tokuro Matsuo, Haeng Kon Kim, Hyun Yeo, Chung Sun Hong, Naoki Fukuta, Takayuki Ito, Huaikou Miao
出版者Institute of Electrical and Electronics Engineers Inc.
ページ117-122
ページ数6
ISBN(印刷物)9781538658895
DOI
出版物ステータスPublished - 2018 8 20
イベント19th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, SNPD 2018 - Busan, Korea, Republic of
継続期間: 2018 6 272018 6 29

出版物シリーズ

名前Proceedings - 2018 IEEE/ACIS 19th International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, SNPD 2018

Other

Other19th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, SNPD 2018
Korea, Republic of
Busan
期間18/6/2718/6/29

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Hardware and Architecture
  • Software
  • Control and Optimization
  • Information Systems and Management

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  • これを引用

    Luo, J., Baz, D. E., & Hu, J. (2018). Acceleration of a CUDA-based hybrid genetic algorithm and its application to a flexible flow shop scheduling problem. : H. J. Hwang, L. Cai, G. H. Yeom, T. Matsuo, H. K. Kim, H. Yeo, C. S. Hong, N. Fukuta, T. Ito, & H. Miao (版), Proceedings - 2018 IEEE/ACIS 19th International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, SNPD 2018 (pp. 117-122). [8441112] (Proceedings - 2018 IEEE/ACIS 19th International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, SNPD 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SNPD.2018.8441112