KT-EGO: a knowledge transfer assisted efficient global optimization algorithm for solving high-dimensional expensive black-box problems-Reference-Cited by-同舟云学术

KT-EGO: a knowledge transfer assisted efficient global optimization algorithm for solving high-dimensional expensive black-box problems

Published:2022-11-11 Issue: Volume: Page:1-19
ISSN:0305-215X
Container-title:Engineering Optimization
language:en
Short-container-title:Engineering Optimization

Author:

Wang Qineng¹,Song Liming¹,Chen Yun²,Ma Guangjian²,Guo Zhendong¹,Li Jun¹

Affiliation:

1. Institute of Turbomachinery, Xi'an Jiaotong University, Xi'an, P.R. China

2. AVIC Shenyang Engine Design Institute, Shenyang, P.R. China

Funder

National Science and Technology Major Project

National Science Foundation of China

Industry-University-Research Cooperation Project of Aero Engine Corporation 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.2022.2139374

Reference41 articles.

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3. Efficient Generalized Surrogate-Assisted Evolutionary Algorithm for High-Dimensional Expensive Problems

4. Chen, Wenxiang, Thomas Weise, Zhenyu Yang, and Ke Tang. 2010. “Large-Scale Global Optimization Using Cooperative Coevolution with Variable Interaction Learning.” In Proceedings of Parallel Problem Solving from Nature (PPSN XI), edited by Robert Schaefer, Carlos Cotta, Joanna Kołodziej, and Günter Rudolph, 300–309. Berlin: Springer.

5. Hybrid surrogate-based optimization using space reduction (HSOSR) for expensive black-box functions

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