A fast global optimizer based on improved CS-RBF and stochastic optimal algorithm

Siu Lau Ho; Shiyou Yang; Guangzheng Ni; H. C. Wong

doi:10.1109/TMAG.2006.871429

A fast global optimizer based on improved CS-RBF and stochastic optimal algorithm

Siu Lau Ho
, Shiyou Yang
, Guangzheng Ni
, H. C. Wong

The Hong Kong Polytechnic University

Research output: Journal article publication › Journal article › Academic research › peer-review

4 Citations (Scopus)

Abstract

An improved compactly supported radial basis function is proposed as a response surface model in the study of computationally heavy design problems. A new interpolation formula is introduced to enhance the interpolation accuracy on boundary derivatives and the proposed response surface model is then combined with stochastic algorithms in the design of a fast global optimizer. Numerical results are reported to demonstrate the generality and the robustness of the proposed works.

Original language	English
Pages (from-to)	1175-1178
Number of pages	4
Journal	IEEE Transactions on Magnetics
Volume	42
Issue number	4
DOIs	https://doi.org/10.1109/TMAG.2006.871429
Publication status	Published - 1 Apr 2006

Keywords

Compact support
Global optimization
Inverse problem
Radial basis function
Response surface model

ASJC Scopus subject areas

Electrical and Electronic Engineering
Physics and Astronomy (miscellaneous)

More information

10.1109/TMAG.2006.871429

http://hdl.handle.net/10397/893

Cite this

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title = "A fast global optimizer based on improved CS-RBF and stochastic optimal algorithm",

abstract = "An improved compactly supported radial basis function is proposed as a response surface model in the study of computationally heavy design problems. A new interpolation formula is introduced to enhance the interpolation accuracy on boundary derivatives and the proposed response surface model is then combined with stochastic algorithms in the design of a fast global optimizer. Numerical results are reported to demonstrate the generality and the robustness of the proposed works.",

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AU - Ni, Guangzheng

AU - Wong, H. C.

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AB - An improved compactly supported radial basis function is proposed as a response surface model in the study of computationally heavy design problems. A new interpolation formula is introduced to enhance the interpolation accuracy on boundary derivatives and the proposed response surface model is then combined with stochastic algorithms in the design of a fast global optimizer. Numerical results are reported to demonstrate the generality and the robustness of the proposed works.

KW - Compact support

KW - Global optimization

KW - Inverse problem

KW - Radial basis function

KW - Response surface model

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U2 - 10.1109/TMAG.2006.871429

DO - 10.1109/TMAG.2006.871429

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JO - IEEE Transactions on Magnetics

JF - IEEE Transactions on Magnetics

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ER -

A fast global optimizer based on improved CS-RBF and stochastic optimal algorithm

Abstract

Keywords

ASJC Scopus subject areas

More information

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