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A Hybrid Replacement Strategy for MOEA/D

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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

In MOEA/D, the replacement strategy plays a key role in balancing diversity and convergence. However, existing adaptive replacement strategies either focus on neighborhood or global replacement strategy, which may have no obvious effects on balance of diversity and convergence in tackling complicated MOPs. In order to overcome this shortcoming, we propose a hybrid mechanism balancing neighborhood and global replacement strategy. In this mechanism, a probability threshold \( p_{t} \) is applied to determine whether to execute a neighborhood or global replacement strategy, which could balance diversity and convergence. Furthermore, we design an offspring generation method to generate the high-quality solution for each subproblem, which can ease mismatch between subproblems and solutions. Based on the classic MOEA/D, we design a new algorithm framework, called MOEA/D-HRS. Compared with other state-of-the-art MOEAs, experimental results show that the proposed algorithm obtains the best performance.

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Acknowledgments

This work is supported in part by the National Natural Science Foundation of China (No. 61375058, 61673397), and the Co-construction Project of Beijing Municipal Commission of Education.

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Authors and Affiliations

  1. Beijing Key Lab of Intelligent Telecommunications Software and Multimedia, Beijing University of Posts and Telecommunications, Beijing, 100876, China

    Xiaoji Chen, Chuan Shi, Aimin Zhou, Siyong Xu & Bin Wu

  2. Shanghai Key Laboratory of Multidimensional Information Processing, Department of Computer Science and Technology, East China Normal University, Shanghai, 200062, China

    Xiaoji Chen, Chuan Shi, Aimin Zhou, Siyong Xu & Bin Wu

Authors
  1. Xiaoji Chen
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  2. Chuan Shi
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  3. Aimin Zhou
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  4. Siyong Xu
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  5. Bin Wu
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Corresponding author

Correspondence to Chuan Shi .

Editor information

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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Chen, X., Shi, C., Zhou, A., Xu, S., Wu, B. (2018). A Hybrid Replacement Strategy for MOEA/D. 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_22

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  • DOI: https://doi.org/10.1007/978-981-13-2826-8_22

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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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