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Comparison of parallel infill sampling criteria based on Kriging surrogate model

One of the key issues that affect the optimization effect of the efficient global optimization (EGO) algorithm is to determine the infill sampling criterion. Therefore, this paper compares the common efficient parallel infill sampling criterion. In addition, the pseudo-expected improvement (EI) crit...

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Autores principales: Chen, Cong, Liu, Jiaxin, Xu, Pingfei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8758680/
https://www.ncbi.nlm.nih.gov/pubmed/35027579
http://dx.doi.org/10.1038/s41598-021-04553-5
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author Chen, Cong
Liu, Jiaxin
Xu, Pingfei
author_facet Chen, Cong
Liu, Jiaxin
Xu, Pingfei
author_sort Chen, Cong
collection PubMed
description One of the key issues that affect the optimization effect of the efficient global optimization (EGO) algorithm is to determine the infill sampling criterion. Therefore, this paper compares the common efficient parallel infill sampling criterion. In addition, the pseudo-expected improvement (EI) criterion is introduced to minimizing the predicted (MP) criterion and the probability of improvement (PI) criterion, which helps to improve the problem of MP criterion that is easy to fall into local optimum. An adaptive distance function is proposed, which is used to avoid the concentration problem of update points and also improves the global search ability of the infill sampling criterion. Seven test problems were used to evaluate these criteria to verify the effectiveness of these methods. The results show that the pseudo method is also applicable to PI and MP criteria. The DMP and PEI criteria are the most efficient and robust. The actual engineering optimization problems can more directly show the effects of these methods. So these criteria are applied to the inverse design of RAE2822 airfoil. The results show the criterion including the MP has higher optimization efficiency.
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spelling pubmed-87586802022-01-14 Comparison of parallel infill sampling criteria based on Kriging surrogate model Chen, Cong Liu, Jiaxin Xu, Pingfei Sci Rep Article One of the key issues that affect the optimization effect of the efficient global optimization (EGO) algorithm is to determine the infill sampling criterion. Therefore, this paper compares the common efficient parallel infill sampling criterion. In addition, the pseudo-expected improvement (EI) criterion is introduced to minimizing the predicted (MP) criterion and the probability of improvement (PI) criterion, which helps to improve the problem of MP criterion that is easy to fall into local optimum. An adaptive distance function is proposed, which is used to avoid the concentration problem of update points and also improves the global search ability of the infill sampling criterion. Seven test problems were used to evaluate these criteria to verify the effectiveness of these methods. The results show that the pseudo method is also applicable to PI and MP criteria. The DMP and PEI criteria are the most efficient and robust. The actual engineering optimization problems can more directly show the effects of these methods. So these criteria are applied to the inverse design of RAE2822 airfoil. The results show the criterion including the MP has higher optimization efficiency. Nature Publishing Group UK 2022-01-13 /pmc/articles/PMC8758680/ /pubmed/35027579 http://dx.doi.org/10.1038/s41598-021-04553-5 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Chen, Cong
Liu, Jiaxin
Xu, Pingfei
Comparison of parallel infill sampling criteria based on Kriging surrogate model
title Comparison of parallel infill sampling criteria based on Kriging surrogate model
title_full Comparison of parallel infill sampling criteria based on Kriging surrogate model
title_fullStr Comparison of parallel infill sampling criteria based on Kriging surrogate model
title_full_unstemmed Comparison of parallel infill sampling criteria based on Kriging surrogate model
title_short Comparison of parallel infill sampling criteria based on Kriging surrogate model
title_sort comparison of parallel infill sampling criteria based on kriging surrogate model
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8758680/
https://www.ncbi.nlm.nih.gov/pubmed/35027579
http://dx.doi.org/10.1038/s41598-021-04553-5
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