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International Conference on Evolutionary Multi-Criterion Optimization

EMO 2021: Evolutionary Multi-Criterion Optimization pp 519–530Cite as

Investigating Normalization Bounds for Hypervolume-Based Infill Criterion for Expensive Multiobjective Optimization

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12654))

Abstract

While solving expensive multi-objective optimization problems, there may be stringent limits on the number of allowed function evaluations. Surrogate models are commonly used for such problems where calls to surrogates are made in lieu of calls to the true objective functions. The surrogates can also be used to identify infill points for evaluation, i.e., solutions that maximize certain performance criteria. One such infill criteria is the maximization of predicted hypervolume, which is the focus of this study. In particular, we are interested in investigating if better estimate of the normalization bounds could help in improving the performance of the surrogate assisted optimization algorithm. Towards this end, we propose a strategy to identify a better ideal point than the one that exists in the current archive. Numerical experiments are conducted on a range of problems to test the efficacy of the proposed method. The approach outperforms conventional forms of normalization in some cases, while providing comparable results for others. We provide critical insights on the search behavior and relate them with the underlying properties of the test problems.

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Acknowledgements

The authors would like to acknowledge Discovery Project DP190102591 from the Australian Research Council.

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Authors and Affiliations

  1. School of Engineering and Information Technology, The University of New South Wales, Canberra, ACT, 2600, Australia

    Bing Wang, Hemant Kumar Singh & Tapabrata Ray

Authors
  1. Bing Wang
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  2. Hemant Kumar Singh
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  3. Tapabrata Ray
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Corresponding author

Correspondence to Bing Wang .

Editor information

Editors and Affiliations

  1. Southern University of Science and Technology, Shenzhen, China

    Hisao Ishibuchi

  2. City University of Hong Kong, Kowloon Tong, China

    Qingfu Zhang

  3. Southern University of Science and Technology, Shenzhen, China

    Ran Cheng

  4. University of Exeter, Exeter, UK

    Ke Li

  5. Xi'an Jiaotong University, Xi'an, China

    Hui Li

  6. Xidian University, Xi'an, China

    Handing Wang

  7. East China Normal University, Shanghai, China

    Aimin Zhou

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Wang, B., Singh, H.K., Ray, T. (2021). Investigating Normalization Bounds for Hypervolume-Based Infill Criterion for Expensive Multiobjective Optimization. In: Ishibuchi, H., et al. Evolutionary Multi-Criterion Optimization. EMO 2021. Lecture Notes in Computer Science(), vol 12654. Springer, Cham. https://doi.org/10.1007/978-3-030-72062-9_41

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  • DOI: https://doi.org/10.1007/978-3-030-72062-9_41

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-72061-2

  • Online ISBN: 978-3-030-72062-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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