A dynamic Gaussian process surrogate model-assisted particle swarm optimisation algorithm for expensive structural optimisation problems-Reference-Cited by-同舟云学术

A dynamic Gaussian process surrogate model-assisted particle swarm optimisation algorithm for expensive structural optimisation problems

Published:2022-03-11 Issue:1 Volume:27 Page:416-436
ISSN:1964-8189
Container-title:European Journal of Environmental and Civil Engineering
language:en
Short-container-title:European Journal of Environmental and Civil Engineering

Author:

Luo Danni¹²,Huang Jie²,Su Guoshao²,Tao Honghui¹

Affiliation:

1. China Energy Engineering Group Guangxi Electric Power Design Institute Co., Ltd., Nanning , China

2. Key Laboratory of Disaster Prevention and Structural Safety of Ministry of Education, School of Civil and Architecture Engineering, Guangxi University, Nanning, Guangxi, China

Funder

National Natural Science Foundation of China

China Postdoctoral Science Foundation

Guangxi Natural Science Foundation

Publisher

Informa UK Limited

Subject

Civil and Structural Engineering,Environmental Engineering

Link

https://www.tandfonline.com/doi/pdf/10.1080/19648189.2022.2049371

Reference58 articles.

1. Innovative adaptive penalty in surrogate-assisted robust optimization of blade attachments

2. Novel approach to predicting blast-induced ground vibration using Gaussian process regression

3. Optimization of Short Load Forecasting in Electricity Market of Iran Using Artificial Neural Networks

4. Comparative Study of SVMs and ANNs in Aquifer Water Level Prediction

5. Efficient Global Reliability Analysis for Nonlinear Implicit Performance Functions

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