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Multi-fidelity surrogate model ensemble based on feasible intervals

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Structural and Multidisciplinary Optimization Aims and scope Submit manuscript

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.

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Funding

This research is funded by the National Key Research and Development Program of China (No. 2018YFB1700704) and the National Natural Science Foundation of China (No. 52075068).

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Authors and Affiliations

  1. School of Mechanical Engineering, Dalian University of Technology, Dalian, 116024, China

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

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  1. Shuai Zhang
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  2. Pengwei Liang
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  3. Yong Pang
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  4. Jianji Li
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  5. Xueguan Song
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Correspondence to Xueguan Song.

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Replication of results

The results provided in this paper are generated by MATLAB codes developed by the authors. The codes can be available upon request by contacting the corresponding author via email.

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Responsible Editor: Erdem Acar

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Appendix

Appendix

See Table 2.

Table 2 20 Test functions
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Zhang, S., Liang, P., Pang, Y. et al. Multi-fidelity surrogate model ensemble based on feasible intervals. Struct Multidisc Optim 65, 212 (2022). https://doi.org/10.1007/s00158-022-03329-3

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  • DOI: https://doi.org/10.1007/s00158-022-03329-3

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