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Enhanced Biogeography-Based Optimization for Flow-Shop Scheduling

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Bio-inspired Computing: Theories and Applications (BIC-TA 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 951))

Abstract

Flow-shop scheduling problem (FSP) is a well-known NP-hard combinatorial optimization problem that occurs in many practical applications. Traditional algorithms are only capable of solving small-size FSP instances, and thus many metaheuristic algorithms have been proposed for efficiently solving large-size instances. However, most existing algorithms still suffer from low convergence speed and/or premature convergence. In this paper, we propose an enhanced biogeography-based optimization (BBO) algorithm framework for FSP, which uses the largest ranked value representation for solution encoding, employs the NEH method to improve the initial population, and designs a reinsertion local search operator based on the job with the longest waiting time (JLWT) to enhance exploitation ability. We respectively use the original BBO migration, blended migration, hybrid BBO and DE migration, and ecogeography-based migration to implement the framework. Experimental results on test instances demonstrate the effectiveness of the proposed BBO algorithms, among which the ecogeography-based optimization (EBO) algorithm version exhibits the best performance.

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Acknowledgements

This work is supported by National Natural Science Foundation (Grant No. 61473263) and Zhejiang Provincial Natural Science Foundation (Grant No. LY14F030011) of China.

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Authors and Affiliations

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

    Yi-Chen Du, Min-Xia Zhang, Ci-Yun Cai & Yu-Jun Zheng

  2. Institute of Service Engineering, Hangzhou Normal University, Hangzhou, 311121, China

    Yu-Jun Zheng

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  1. Yi-Chen Du
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  2. Min-Xia Zhang
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  3. Ci-Yun Cai
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  4. Yu-Jun Zheng
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Corresponding author

Correspondence to Yu-Jun Zheng .

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Editors and Affiliations

  1. Beijing University of Posts and Telecommunications, Beijing, China

    Jianyong Qiao

  2. Beijing University of Posts and Telecommunications, Beijing, China

    Xinchao Zhao

  3. Huazhong University of Science and Technology, Wuhan, China

    Linqiang Pan

  4. Beijing University of Posts and Telecommunications, Beijing, China

    Xingquan Zuo

  5. Anhui University, Hefei, China

    Xingyi Zhang

  6. City University of Hong Kong, Kowloon, Hong Kong

    Qingfu Zhang

  7. Beijing University of Posts and Telecommunications, Beijing, China

    Shanguo Huang

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Du, YC., Zhang, MX., Cai, CY., Zheng, YJ. (2018). Enhanced Biogeography-Based Optimization for Flow-Shop Scheduling. In: Qiao, J., et al. Bio-inspired Computing: Theories and Applications. BIC-TA 2018. Communications in Computer and Information Science, vol 951. Springer, Singapore. https://doi.org/10.1007/978-981-13-2826-8_26

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  • DOI: https://doi.org/10.1007/978-981-13-2826-8_26

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-2825-1

  • Online ISBN: 978-981-13-2826-8

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