Trust-region based adaptive radial basis function algorithm for global optimization of expensive constrained black-box problems

2021 ◽  
Vol 105 ◽  
pp. 107233
Author(s):  
Chengyang Liu ◽  
Zhiqiang Wan ◽  
Yijie Liu ◽  
Xuewu Li ◽  
Dianzi Liu
Keyword(s):  
Global Optimization ◽  
Radial Basis Function ◽  
Basis Function ◽  
Trust Region ◽  
Black Box ◽  
Radial Basis

scholarly journals Efficient Solving of Boundary Value Problems Using Radial Basis Function Networks Learned by Trust Region Method

2018 ◽  
Vol 2018 ◽  
pp. 1-4 ◽  
Author(s):  
Mohie Mortadha Alqezweeni ◽  
Vladimir Ivanovich Gorbachenko ◽  
Maxim Valerievich Zhukov ◽  
Mustafa Sadeq Jaafar
Keyword(s):  
Radial Basis Function ◽  
Boundary Value Problems ◽  
Basis Function ◽  
Boundary Value ◽  
Trust Region Method ◽  
Trust Region ◽  
Radial Basis Function Networks ◽  
Structure Selection ◽  
Region Method ◽  
Radial Basis

A method using radial basis function networks (RBFNs) to solve boundary value problems of mathematical physics is presented in this paper. The main advantages of mesh-free methods based on RBFN are explained here. To learn RBFNs, the Trust Region Method (TRM) is proposed, which simplifies the process of network structure selection and reduces time expenses to adjust their parameters. Application of the proposed algorithm is illustrated by solving two-dimensional Poisson equation.

Adaptive Radial-Basis-Function-Based Multifidelity Metamodeling for Expensive Black-Box Problems

AIAA Journal ◽  
2017 ◽  
Vol 55 (7) ◽  
pp. 2424-2436 ◽  
Author(s):  
Xiwen Cai ◽  
Haobo Qiu ◽  
Liang Gao ◽  
Li Wei ◽  
Xinyu Shao
Keyword(s):  
Radial Basis Function ◽  
Basis Function ◽  
Black Box ◽  
Radial Basis
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