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Solving Three-Objective Flow Shop Problem with Fast Hypervolume-Based Local Search Algorithm

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Intelligent Computing Methodologies (ICIC 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8589))

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Abstract

In this paper, we present a fast hypervolume-based multi-objective local search algorithm, where the fitness assignment is realized by the approximating computation of hypervolume contribution. In the algorithm, we define an approximate hypervolume contribution indicator as the selection mechanism and apply this indicator to an iterated local search. We carry out a range of experiments on three-objective flow shop problem. Experimental results indicate that our algorithm is highly effective in comparison with the algorithms based on the binary indicators and the exact hypervolume contribution indicator.

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Author information

Authors and Affiliations

  1. School of Mathematics, Southwest Jiaotong University, Chengdu, Sichuan, 610031, P.R. China

    Rong-Qiang Zeng

  2. School of Computer Science and Engineering, University of Electronic, Science and Technology of China, Chengdu, Sichuan, 610054, P.R. China

    Rong-Qiang Zeng & Ming-Sheng Shang

Authors
  1. Rong-Qiang Zeng
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  2. Ming-Sheng Shang
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Editor information

Editors and Affiliations

  1. School of Electronics and Information Engineering, Tongji University, 4800 Caoan Road, 201804, Shanghai, China

    De-Shuang Huang

  2. School of Electrical Engineering, University of Ulsan, 680–749, Ulsan, Korea

    Kang-Hyun Jo

  3. Department of Automation, Tsinghua University, Bejing, China

    Ling Wang

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Zeng, RQ., Shang, MS. (2014). Solving Three-Objective Flow Shop Problem with Fast Hypervolume-Based Local Search Algorithm. In: Huang, DS., Jo, KH., Wang, L. (eds) Intelligent Computing Methodologies. ICIC 2014. Lecture Notes in Computer Science(), vol 8589. Springer, Cham. https://doi.org/10.1007/978-3-319-09339-0_2

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  • DOI: https://doi.org/10.1007/978-3-319-09339-0_2

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-09338-3

  • Online ISBN: 978-3-319-09339-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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