Multi-fidelity surrogate model ensemble based on feasible intervals View Full Text


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Article Info

DATE

2022-07-27

AUTHORS

Shuai Zhang, Pengwei Liang, Yong Pang, Jianji Li, Xueguan Song

ABSTRACT

Multi-fidelity surrogate models received a lot of attention in engineering optimization due to their ability to achieve the required accuracy at a lower cost. However, selecting an appropriate scale factor to improve the prediction accuracy remains a challenge. As a result, this paper proposes a novel method for determining the scale factor. Unlike previous studies, the proposed method uses feasible intervals to determine a series of scaling factors and corresponding multi-fidelity surrogate models. Then, the ensemble of multi-fidelity surrogate models is used to improve prediction accuracy. Twenty test functions and an engineering problem are used to validate the proposed model. The results show that this model outperforms the other multi-fidelity surrogate models in terms of prediction accuracy and robustness. Furthermore, the impact of various cost ratios and proportions on the performance of the proposed model is investigated. Once again, it demonstrates a higher priority than the other models. This work provides a new approach to the design and optimization of engineering problems. More... »

PAGES

212

References to SciGraph publications

  • 2017-03-17. The numerical solution of two-dimensional logarithmic integral equations on normal domains using radial basis functions with polynomial precision in ENGINEERING WITH COMPUTERS
  • 2020-01-23. A global optimization strategy based on the Kriging surrogate model and parallel computing in STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
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  • 2019-04-06. A sequential constraints updating approach for Kriging surrogate model-assisted engineering optimization design problem in ENGINEERING WITH COMPUTERS
  • 2017-05-04. Multifidelity surrogate modeling based on radial basis functions in STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
  • 2018-06-23. Low-fidelity scale factor improves Bayesian multi-fidelity prediction by reducing bumpiness of discrepancy function in STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
  • 2018-02-26. Gradient-enhanced kriging for high-dimensional problems in ENGINEERING WITH COMPUTERS
  • 2006-07-08. Update strategies for kriging models used in variable fidelity optimization in STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
  • 2021-08-18. A multi-fidelity surrogate model based on moving least squares: fusing different fidelity data for engineering design in STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
  • 2001-12. Comparative studies of metamodelling techniques under multiple modelling criteria in STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s00158-022-03329-3

    DOI

    http://dx.doi.org/10.1007/s00158-022-03329-3

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1149816364


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