A Weighted Sequential Sampling Method Considering Influences of Sample Qualities in Input and Output Parameter Spaces for Global Optimization

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.

[1]  Søren Nymand Lophaven,et al.  DACE - A Matlab Kriging Toolbox, Version 2.0 , 2002 .

[2]  Wei Chen,et al.  A New Variable-Fidelity Optimization Framework Based on Model Fusion and Objective-Oriented Sequential Sampling , 2007, DAC 2007.

[3]  M. D. McKay,et al.  A comparison of three methods for selecting values of input variables in the analysis of output from a computer code , 2000 .

[4]  Ruichen Jin,et al.  On Sequential Sampling for Global Metamodeling in Engineering Design , 2002, DAC 2002.

[5]  Runze Li,et al.  Design and Modeling for Computer Experiments (Computer Science & Data Analysis) , 2005 .

[6]  Wen Yao,et al.  A gradient-based sequential radial basis function neural network modeling method , 2009, Neural Computing and Applications.

[7]  G. Matheron.  Principles of geostatistics , 1963 .

[8]  Thomas Hochkirchen.  Design and Modeling for Computer Experiments by K.-T. Fang, R. Li and A. Sudjianto , 2006 .

[9]  Runze Li,et al.  Design and Modeling for Computer Experiments , 2005 .

[10]  Søren Nymand Lophaven,et al.  DACE - A Matlab Kriging Toolbox , 2002 .

[11]  Robert B. Gramacy,et al.  Adaptive Design and Analysis of Supercomputer Experiments , 2008, Technometrics.

[12]  Thomas J. Santner,et al.  The Design and Analysis of Computer Experiments , 2003, Springer Series in Statistics.

[13]  Dong Zhao,et al.  A comparative study of metamodeling methods considering sample quality merits , 2010 .

[14]  G. Gary Wang,et al.  Review of Metamodeling Techniques in Support of Engineering Design Optimization , 2007 .

[15]  Timothy W. Simpson,et al.  Metamodels for Computer-based Engineering Design: Survey and recommendations , 2001, Engineering with Computers.

[16]  Fred J. Hickernell,et al.  A generalized discrepancy and quadrature error bound , 1998, Math. Comput..

[17]  Deyi Xue,et al.  A semi-empirical model considering the influence of operating parameters on performance for a direct methanol fuel cell , 2011 .

[18]  Xin Wei,et al.  A new sequential optimal sampling method for radial basis functions , 2012, Appl. Math. Comput..