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

Jia Luo, Didier El Baz, Takayuki Furuzuki

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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. 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.

Original languageEnglish
Title of host publicationProceedings - 2018 IEEE/ACIS 19th International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, SNPD 2018
EditorsHa Jin Hwang, Lizhi Cai, Gun Huck Yeom, Tokuro Matsuo, Haeng Kon Kim, Hyun Yeo, Chung Sun Hong, Naoki Fukuta, Takayuki Ito, Huaikou Miao
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages117-122
Number of pages6
ISBN (Print)9781538658895
DOIs
Publication statusPublished - 2018 Aug 20
Event19th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, SNPD 2018 - Busan, Korea, Republic of
Duration: 2018 Jun 272018 Jun 29

Other

Other19th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, SNPD 2018
CountryKorea, Republic of
CityBusan
Period18/6/2718/6/29

Fingerprint

Hybrid Genetic Algorithm
Flow Shop Scheduling
Scheduling Problem
Genetic algorithms
Scheduling
Execution Time
Parallel algorithms
Island Model
Parallel Genetic Algorithm
Hybrid Method
Control Parameter
Migration
Speedup
Limiting
Numerical Experiment
Genetic Algorithm
Optimization Problem
Interval
Experiments
Range of data

Keywords

  • CUDA
  • Flexible Flow Shop Scheduling
  • GPU computing
  • Parallel Genetic Algorithm

ASJC Scopus subject areas

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

Cite this

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

Acceleration of a CUDA-based hybrid genetic algorithm and its application to a flexible flow shop scheduling problem. / Luo, Jia; Baz, Didier El; Furuzuki, Takayuki.

Proceedings - 2018 IEEE/ACIS 19th International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, SNPD 2018. ed. / Ha Jin Hwang; Lizhi Cai; Gun Huck Yeom; Tokuro Matsuo; Haeng Kon Kim; Hyun Yeo; Chung Sun Hong; Naoki Fukuta; Takayuki Ito; Huaikou Miao. Institute of Electrical and Electronics Engineers Inc., 2018. p. 117-122 8441112.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Luo, J, Baz, DE & Furuzuki, T 2018, Acceleration of a CUDA-based hybrid genetic algorithm and its application to a flexible flow shop scheduling problem. in HJ Hwang, L Cai, GH Yeom, T Matsuo, HK Kim, H Yeo, CS Hong, N Fukuta, T Ito & H Miao (eds), Proceedings - 2018 IEEE/ACIS 19th International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, SNPD 2018., 8441112, Institute of Electrical and Electronics Engineers Inc., pp. 117-122, 19th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, SNPD 2018, Busan, Korea, Republic of, 18/6/27. https://doi.org/10.1109/SNPD.2018.8441112
Luo J, Baz DE, Furuzuki T. Acceleration of a CUDA-based hybrid genetic algorithm and its application to a flexible flow shop scheduling problem. In Hwang HJ, Cai L, Yeom GH, Matsuo T, Kim HK, Yeo H, Hong CS, Fukuta N, Ito T, Miao H, editors, 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. 2018. p. 117-122. 8441112 https://doi.org/10.1109/SNPD.2018.8441112
Luo, Jia ; Baz, Didier El ; Furuzuki, Takayuki. / Acceleration of a CUDA-based hybrid genetic algorithm and its application to a flexible flow shop scheduling problem. Proceedings - 2018 IEEE/ACIS 19th International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, SNPD 2018. editor / Ha Jin Hwang ; Lizhi Cai ; Gun Huck Yeom ; Tokuro Matsuo ; Haeng Kon Kim ; Hyun Yeo ; Chung Sun Hong ; Naoki Fukuta ; Takayuki Ito ; Huaikou Miao. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 117-122
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