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CSS–A Cheap-Surrogate-Based Selection Operator for Multi-objective Optimization

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Bioinspired Optimization Methods and Their Applications (BIOMA 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13627))

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Abstract

Due to the complex topology of the search space, expensive multi-objective evolutionary algorithms (EMOEAs) emphasize enhancing the exploration capability. Many algorithms use ensembles of surrogate models to boost the performance. Generally, the surrogate-based model either works out the solution’s fitness by approximating the evaluation function or selects the solution by weighting the uncertainty degree of candidate solutions. This paper proposes a selection operator called Cheap surrogate selection (CSS) for multi-objective problems by utilizing the density probability on a k-dimensional tree. As opposed to the first type of surrogate models, which approximate the objective function, the proposed CSS only estimates the uncertainty of the candidate solutions. As a result, CSS does not require extensive sampling or training. Besides, CSS makes use of neighbors’ density and builds the tree with low computational complexity, resulting in an accelerated surrogate process. Moreover, a new EMOEA is proposed by integrating spherical search as the core optimizer with the proposed selection scheme. Over a wide variety of benchmark problems, we show that the proposed method outperforms several state-of-the-art EMOEAs.

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Notes

  1. 1.

    code link: https://drive.google.com/drive/folders/14uif4ozlZCrJ8EAi4FrlUUk9GKrh72IR?usp=sharing.

  2. 2.

    https://github.com/BIMK/PlatEMO.

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Acknowlegement

This work was supported by the Ministry of Education, Youth and Sports of the Czech Republic in project META MO-COP, reg. No. LTAIN19176.

Author information

Authors and Affiliations

  1. Department of Computer Science, VSB-Technical University of Ostrava, Ostrava, Czechia

    Lingping Kong, Václav Snášel & Pavel Krömer

  2. Department of Artificial Intelligence, Kyungpook National University, Daegu, 701702, Republic of Korea

    Abhishek Kumar

  3. Electronics and Communication Sciences Unit, Indian Statistical Institute, 203, B. T. Road, Kolkata, 700108, India

    Swagatam Das

  4. Department of Computer Science, University of Reading, Reading, UK

    Varun Ojha

Authors
  1. Lingping Kong
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  2. Abhishek Kumar
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  3. Václav Snášel
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  4. Swagatam Das
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  5. Pavel Krömer
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  6. Varun Ojha
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Correspondence to Václav Snášel or Swagatam Das .

Editor information

Editors and Affiliations

  1. University of Maribor, Maribor, Slovenia

    Marjan Mernik

  2. Jožef Stefan Institute, Ljubljana, Slovenia

    Tome Eftimov

  3. University of Maribor, Maribor, Slovenia

    Matej Črepinšek

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Kong, L., Kumar, A., Snášel, V., Das, S., Krömer, P., Ojha, V. (2022). CSS–A Cheap-Surrogate-Based Selection Operator for Multi-objective Optimization. In: Mernik, M., Eftimov, T., Črepinšek, M. (eds) Bioinspired Optimization Methods and Their Applications. BIOMA 2022. Lecture Notes in Computer Science, vol 13627. Springer, Cham. https://doi.org/10.1007/978-3-031-21094-5_5

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  • DOI: https://doi.org/10.1007/978-3-031-21094-5_5

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