An efficient global optimization algorithm combining revised expectation improvement criteria and Kriging-Reference-Cited by-同舟云学术

An efficient global optimization algorithm combining revised expectation improvement criteria and Kriging

Published:2023-02-22 Issue: Volume: Page:1-17
ISSN:0305-215X
Container-title:Engineering Optimization
language:en
Short-container-title:Engineering Optimization

Author:

Liu Zecong¹,Huang Hanyan¹,Xu Xiaoyu¹,Xiong Mei¹,Li Qizhe¹

Affiliation:

1. School of Systems Science and Engineering, Sun Yat-sen University, Guangzhou, People's Republic of China

Funder

Sun Yat-Sen University

National Nature Science Foundation of China

Publisher

Informa UK Limited

Subject

Applied Mathematics,Industrial and Manufacturing Engineering,Management Science and Operations Research,Control and Optimization,Computer Science Applications

Link

https://www.tandfonline.com/doi/pdf/10.1080/0305215X.2023.2170367

Reference42 articles.

1. Stochastic Kriging for Simulation Metamodeling

2. Hybrid surrogate-model-based multi-fidelity efficient global optimization applied to helicopter blade design

3. Robust Gaussian Process-Based Global Optimization Using a Fully Bayesian Expected Improvement Criterion

4. Chen, Zhehui, Simon Mak, and C. F. Wu. 2019. “A Hierarchical Expected Improvement Method for Bayesian Optimization.” arXiv: 1911.07285.

5. Ensemble of surrogates with hybrid method using global and local measures for engineering design

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