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Pareto-Based Bi-indicator Infill Sampling Criterion for Expensive Multiobjective Optimization

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Evolutionary Multi-Criterion Optimization (EMO 2021)

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

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Abstract

Infill sampling criteria play a crucial role in saving expensive evaluations for surrogate-assisted multiobjective evolutionary algorithms. Promoting convergence and maintaining diversity in the population are the two main goals of designing a new infilling sampling criterion, which is naturally a bi-objective optimization problem. In this paper, a Pareto-based bi-indicator infill sampling criterion is proposed to select candidate solutions which will be re-evaluated using expensive objective functions. The proposed criterion is embedded into a Gaussian process assisted evolutionary algorithm to solve expensive multiobjective optimization problems. In the proposed criterion, we introduce two indicators measuring the convergence and diversity as two optimization objectives. Empirical studies on the UF and DTLZ problems demonstrate that the proposed algorithm is more effective than other state-of-the-art surrogate-assisted evolutionary algorithms using the same number of expensive function evaluations.

Supported by the National Natural Science Foundation of China (No. 61976165).

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Author information

Authors and Affiliations

  1. School of Artificial Intelligence, Xidian University, Xi’an, 710071, China

    Zhenshou Song & Handing Wang

  2. School of Software Engineering, South China University of Technology, Guangzhou, 510006, China

    Hongbin Xu

Authors
  1. Zhenshou Song
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  2. Handing Wang
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  3. Hongbin Xu
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Corresponding author

Correspondence to Handing 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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Song, Z., Wang, H., Xu, H. (2021). Pareto-Based Bi-indicator Infill Sampling 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_42

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  • DOI: https://doi.org/10.1007/978-3-030-72062-9_42

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-72061-2

  • Online ISBN: 978-3-030-72062-9

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