A high-dimensional optimization method combining projection correlation-based Kriging and multimodal parallel computing-Reference-Cited by-同舟云学术

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A high-dimensional optimization method combining projection correlation-based Kriging and multimodal parallel computing

Published:2022-12-31 Issue:1 Volume:66 Page:
ISSN:1615-147X
Container-title:Structural and Multidisciplinary Optimization
language:en
Short-container-title:Struct Multidisc Optim

Author:

Hao Peng^ORCID,Liu Hao,Feng Shaojun,Wang Guijiao,Zhang Rui,Wang Bo

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Publisher

Springer Science and Business Media LLC

Subject

Control and Optimization,Computer Graphics and Computer-Aided Design,Computer Science Applications,Control and Systems Engineering,Software

Link

https://link.springer.com/content/pdf/10.1007/s00158-022-03450-3.pdf

Reference57 articles.

1. Bachoc F (2013) Cross Validation and Maximum Likelihood estimations of hyper-parameters of Gaussian processes with model misspecification. Comput Stat Data Anal 66:55–69

2. Booker A, Dennis J, Frank P, Serafni D, Torczon V, Trosset M (1999) A rigorous framework for optimization of expensive functions by surrogates. Struct Multidisc Optim 17(1):1–13

3. Bouhlel MA, Bartoli N, Otsmane A, Morlier J (2016a) An Improved approach for estimating the hyperparameters of the Kriging model for high-dimensional problems through the partial least squares method. Math Probl Eng. https://doi.org/10.1155/2016/6723410

4. Bouhlel MA, Bartoli N, Otsmane A, Morlier J (2016b) Improving kriging surrogates of high-dimensional design models by Partial Least Squares dimension reduction. Struct Multidisc Optim 53:935–952

5. Cai XW, Qiu HB, Gao L, Yang P, Shao XY (2016) An enhanced RBF-HDMR integrated with an adaptive sampling method for approximating high dimensional problems in engineering design. Struct Multidisc Optim 53:1209–1229

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