Selecting the Best Quantity and Variety of Surrogates for an Ensemble Model
Surrogate modeling techniques are widely used to replace the computationally expensive black-box functions in engineering. As a combination of individual surrogate models, an ensemble of surrogates is preferred due to its strong robustness. However, how to select the best quantity and variety of sur...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-10-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/8/10/1721 |
| _version_ | 1827704797276405760 |
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| author | Pengcheng Ye Guang Pan |
| author_facet | Pengcheng Ye Guang Pan |
| author_sort | Pengcheng Ye |
| collection | DOAJ |
| description | Surrogate modeling techniques are widely used to replace the computationally expensive black-box functions in engineering. As a combination of individual surrogate models, an ensemble of surrogates is preferred due to its strong robustness. However, how to select the best quantity and variety of surrogates for an ensemble has always been a challenging task. In this work, five popular surrogate modeling techniques including polynomial response surface (PRS), radial basis functions (RBF), kriging (KRG), Gaussian process (GP) and linear shepard (SHEP) are considered as the basic surrogate models, resulting in twenty-six ensemble models by using a previously presented weights selection method. The best ensemble model is expected to be found by comparative studies on prediction accuracy and robustness. By testing eight mathematical problems and two engineering examples, we found that: (1) in general, using as many accurate surrogates as possible to construct ensemble models will improve the prediction performance and (2) ensemble models can be used as an insurance rather than offering significant improvements. Moreover, the ensemble of three surrogates PRS, RBF and KRG is preferred based on the prediction performance. The results provide engineering practitioners with guidance on the superior choice of the quantity and variety of surrogates for an ensemble. |
| first_indexed | 2024-03-10T15:49:15Z |
| format | Article |
| id | doaj.art-e6f42a1313254371bdaa7ce906301e18 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-10T15:49:15Z |
| publishDate | 2020-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-e6f42a1313254371bdaa7ce906301e182023-11-20T16:15:24ZengMDPI AGMathematics2227-73902020-10-01810172110.3390/math8101721Selecting the Best Quantity and Variety of Surrogates for an Ensemble ModelPengcheng Ye0Guang Pan1School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, ChinaSchool of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, ChinaSurrogate modeling techniques are widely used to replace the computationally expensive black-box functions in engineering. As a combination of individual surrogate models, an ensemble of surrogates is preferred due to its strong robustness. However, how to select the best quantity and variety of surrogates for an ensemble has always been a challenging task. In this work, five popular surrogate modeling techniques including polynomial response surface (PRS), radial basis functions (RBF), kriging (KRG), Gaussian process (GP) and linear shepard (SHEP) are considered as the basic surrogate models, resulting in twenty-six ensemble models by using a previously presented weights selection method. The best ensemble model is expected to be found by comparative studies on prediction accuracy and robustness. By testing eight mathematical problems and two engineering examples, we found that: (1) in general, using as many accurate surrogates as possible to construct ensemble models will improve the prediction performance and (2) ensemble models can be used as an insurance rather than offering significant improvements. Moreover, the ensemble of three surrogates PRS, RBF and KRG is preferred based on the prediction performance. The results provide engineering practitioners with guidance on the superior choice of the quantity and variety of surrogates for an ensemble.https://www.mdpi.com/2227-7390/8/10/1721ensemble of surrogatessurrogate modelsprediction accuracyrobustness |
| spellingShingle | Pengcheng Ye Guang Pan Selecting the Best Quantity and Variety of Surrogates for an Ensemble Model Mathematics ensemble of surrogates surrogate models prediction accuracy robustness |
| title | Selecting the Best Quantity and Variety of Surrogates for an Ensemble Model |
| title_full | Selecting the Best Quantity and Variety of Surrogates for an Ensemble Model |
| title_fullStr | Selecting the Best Quantity and Variety of Surrogates for an Ensemble Model |
| title_full_unstemmed | Selecting the Best Quantity and Variety of Surrogates for an Ensemble Model |
| title_short | Selecting the Best Quantity and Variety of Surrogates for an Ensemble Model |
| title_sort | selecting the best quantity and variety of surrogates for an ensemble model |
| topic | ensemble of surrogates surrogate models prediction accuracy robustness |
| url | https://www.mdpi.com/2227-7390/8/10/1721 |
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