Paper Info
Title
A Weighted Sequential Sampling Method Considering Influences of Sample Qualities in Input and Output Parameter Spaces for Global Optimization
Abstract
A new sampling method, namely weighted sequential sampling method, is introduced in this research to improve accuracy and efficiency of adaptive metamodeling considering influences of sample quality measures in both input and output parameter spaces. In this method, sample quality measures in input and output parameter spaces are associated with weighting factors. Values of these weighting factors are changed in sequential sampling considering the different levels of contributions of these sample quality measures in the input and output parameter spaces during the adaptive metamodeling process. Since quality of the metamodel developed through weighted sequential sampling is good in the whole design space, quality of global optimization can be improved through adaptive metamodeling based on weighted sequential sampling.
Year
DOI
Venue
2015
10.1007/s10957-014-0576-9
J. Optimization Theory and Applications
Keywords
Field
DocType
adaptive metamodeling,global optimization,metamodeling,90c26,sequential sampling
Sequential sampling,Design space,Mathematical optimization,Weighting,Global optimization,Input/output,Sampling (statistics),Metamodeling,Mathematics
Journal
Volume
Issue
ISSN
164
2
1573-2878
Citations 
PageRank 
References 
0
0.34
6
Authors
2
Name
Order
Citations
PageRank
Qinwen Yang120.72
Deyi Xue215019.11