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Surrogate-Assisted Evolutionary Optimization of Large Problems

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High-Performance Simulation-Based Optimization

Part of the book series: Studies in Computational Intelligence ((SCI,volume 833))

Abstract

This chapter presents some recent advances in surrogate-assisted evolutionary optimization of large problems. By large problems, we mean either the number of decision variables is large, or the number of objectives is large, or both. These problems pose challenges to evolutionary algorithms themselves, constructing surrogates and surrogate management. To address these challenges, we proposed two algorithms, one called kriging-assisted reference vector guided evolutionary algorithm (K-RVEA) for many-objective optimization, and the other called cooperative swarm optimization algorithm (SA-COSO) for high-dimensional single-objective optimization. Empirical studies demonstrate that K-RVEA works well for many-objective problems having up to ten objectives, while SA-COSA outperforms the state-of-the-art algorithms on 200-dimensional single-objective test problems.

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Acknowledgements

The research of Tinkle Chugh was supported by the FiDiPro project DeCoMo funded by Tekes: Finnish Funding Agency for Innovation and Natural Environment Research Council [grant number NE/P017436/1].

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Authors and Affiliations

  1. University of Jyvaskyla, Faculty of Information Technology, P.O. Box 35 (Agora), 40014, University of Jyvaskyla, Finland

    Tinkle Chugh

  2. Department of Computer Science, University of Exeter, Exeter, UK

    Tinkle Chugh

  3. Department of Computer Science and Technology, Taiyuan University of Science and Technology, Shanxi, 030024, China

    Chaoli Sun

  4. Department of Computer Science, University of Surrey, Guildford, UK

    Handing Wang & Yaochu Jin

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  1. Tinkle Chugh
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  2. Chaoli Sun
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  3. Handing Wang
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  4. Yaochu Jin
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Corresponding author

Correspondence to Yaochu Jin .

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Editors and Affiliations

  1. TH Köln, Cologne, Germany

    Thomas Bartz-Beielstein

  2. Jožef Stefan Institute, Ljubljana, Slovenia

    Bogdan Filipič

  3. Jožef Stefan Institute, Ljubljana, Slovenia

    Peter Korošec

  4. University Lille, Lille, France

    El-Ghazali Talbi

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Chugh, T., Sun, C., Wang, H., Jin, Y. (2020). Surrogate-Assisted Evolutionary Optimization of Large Problems. In: Bartz-Beielstein, T., Filipič, B., Korošec, P., Talbi, EG. (eds) High-Performance Simulation-Based Optimization. Studies in Computational Intelligence, vol 833. Springer, Cham. https://doi.org/10.1007/978-3-030-18764-4_8

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