Influence of coupling factors on structural reliability based on polynomial response surface optimization model

Jiang, Kaiyou; Wang, Junhao; Chen, Zhongquan; Zhou, Qiangqiang; Jiang, Shu-Heng

doi:10.1177/16878132231184145

Cited by 1 publication

(1 citation statement)

References 52 publications

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“…In recent years, various methods have been employed for constructing surrogate model, including polynomial response surface (PRS) [15], support vector regression (SVR) [50], Kriging [11], radial basis function (RBF) [46], artificial neural networks (ANN) [12,10,28], multivariate adaptive regression [49], and random forests [2,3], etc. Building upon this foundation, numerous studies have explored the distinctions between these models and the scenarios in which they are best suited [1,9,14,41], contributing to their widespread application across various engineering domains.…”

Section: Related Workmentioning

confidence: 99%

A Multi-level Surrogate-assisted Algorithm for Expensive Optimization Problems

Hu,

Wu,

Che

2024

ITC

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With the development of computer science, more and more complex problems rely on the help of computers for solving. When facing the parameter optimization problem of complex models, traditional intelligent optimization algorithms often require multiple iterations on the target problem. It can bring unacceptable costs and resource costs in dealing with these complex problems. In order to solve the parameter optimization of complex problems, in this paper we propose a multi-level surrogate-assisted optimization algorithm (MLSAO). By constructing surrogate models at different levels, the algorithm effectively explores the parameter space, avoiding local optima and enhancing optimization efficiency. The method combines two optimization algorithms, differential evolution (DE) and Downhill simplex method. DE is focused on global level surrogate model optimization. Downhill simplex is concentrated on local level surrogate model update. Random forest and inverse distance weighting (IDW) are constructed for global and local level surrogate model respectively. These methods leverage their respective advantages at different stages of the algorithm. The MLSAO algorithm is evaluated against other state-of-the-art approaches using benchmark functions of varying dimensions. Comprehensive results from the comparisons showcase the superior performance of the MLSAO algorithm in addressing expensive optimization problems. Moreover, we implement the MLSAO algorithm for tuning precipitation parameters in the Community Earth System Model (CESM). The outcomes reveal its effective enhancement of CESM's simulation accuracy for precipitation in the North Indian Ocean and the North Pacific region. These experiments demonstrate that MLSAO can better address parameter optimization problems under complex conditions.

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