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Water Wave Optimization for Flow-Shop Scheduling

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Intelligent Computing Methodologies (ICIC 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11645))

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Abstract

Flow-shop scheduling problem (FSP) is a well-known combinatorial optimization problem which has a wide range of practical applications. However, FSP is known to be NP-hard when there are more than two machines, for which traditional exact algorithms can only solve small-size problem instances, and many metaheuristic algorithms are mostly suitable for solving large-size instances. Water wave optimization (WWO) is a novel metaheuristic evolutionary algorithm that draws inspiration from shallow water wave model for optimization problems. In this paper, we propose two WWO algorithms for FSP. The first algorithm adapts the original evolutionary operators of the basic WWO according to the solution space of FSP. The second algorithm further improves the first algorithm with a self-adaptive local search procedure. Experimental results on test instances show that the proposed strategies are effective for solving FSP, and the WWO algorithm with self-adaptive local search exhibits significant performance advantages over many other well-known metaheuristic algorithms.

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Authors and Affiliations

  1. College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, 310023, China

    Jia-Yu Wu, Xue Wu, Xue-Qin Lu, Yi-Chen Du & Min-Xia Zhang

Authors
  1. Jia-Yu Wu
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  2. Xue Wu
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  3. Xue-Qin Lu
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  4. Yi-Chen Du
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  5. Min-Xia Zhang
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Corresponding author

Correspondence to Min-Xia Zhang .

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Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. Nanchang Institute of Technology, Nanchang, China

    Zhi-Kai Huang

  3. Liverpool John Moores University, Liverpool, UK

    Abir Hussain

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Wu, JY., Wu, X., Lu, XQ., Du, YC., Zhang, MX. (2019). Water Wave Optimization for Flow-Shop Scheduling. In: Huang, DS., Huang, ZK., Hussain, A. (eds) Intelligent Computing Methodologies. ICIC 2019. Lecture Notes in Computer Science(), vol 11645. Springer, Cham. https://doi.org/10.1007/978-3-030-26766-7_70

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  • DOI: https://doi.org/10.1007/978-3-030-26766-7_70

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-26765-0

  • Online ISBN: 978-3-030-26766-7

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