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Research Papers

A Robust Error-Pursuing Sequential Sampling Approach for Global Metamodeling Based on Voronoi Diagram and Cross Validation

[+] Author and Article Information
Shengli Xu

School of Energy and Power Engineering,
Dalian University of Technology,
Dalian 116024, China
e-mail: xusl@dlut.edu.cn

Haitao Liu

School of Energy and Power Engineering,
Dalian University of Technology,
Dalian 116024, China
e-mail: lht@mail.dlut.edu.cn

Xiaofang Wang

School of Energy and Power Engineering,
Dalian University of Technology,
Dalian 116024, China
e-mail: dlwxf@dlut.edu.cn

Xiaomo Jiang

School of Energy and Power Engineering,
Dalian University of Technology,
Dalian 116024, China
e-mail: jiang.xiaomo@gmail.com

1Corresponding author.

Contributed by the Design Automation Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received August 2, 2013; final manuscript received February 23, 2014; published online April 28, 2014. Assoc. Editor: Xiaoping Du.

J. Mech. Des 136(7), 071009 (Apr 28, 2014) (10 pages) Paper No: MD-13-1334; doi: 10.1115/1.4027161 History: Received August 02, 2013; Revised February 23, 2014

Surrogate models are widely used in simulation-based engineering design and optimization to save the computing cost. The choice of sampling approach has a great impact on the metamodel accuracy. This article presents a robust error-pursuing sequential sampling approach called cross-validation (CV)-Voronoi for global metamodeling. During the sampling process, CV-Voronoi uses Voronoi diagram to partition the design space into a set of Voronoi cells according to existing points. The error behavior of each cell is estimated by leave-one-out (LOO) cross-validation approach. Large prediction error indicates that the constructed metamodel in this Voronoi cell has not been fitted well and, thus, new points should be sampled in this cell. In order to rapidly improve the metamodel accuracy, the proposed approach samples a Voronoi cell with the largest error value, which is marked as a sensitive region. The sampling approach exploits locally by the identification of sensitive region and explores globally with the shift of sensitive region. Comparative results with several sequential sampling approaches have demonstrated that the proposed approach is simple, robust, and achieves the desired metamodel accuracy with fewer samples, that is needed in simulation-based engineering design problems.

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Copyright © 2014 by ASME
Topics: Design , Errors , Simulation
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Figures

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Fig. 1

A set of 2D samples and the corresponding Voronoi cells

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Fig. 2

A Voronoi cell described by a set of random points, where the circle represents the existing sample and the triangles represent the random points

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Fig. 3

The prediction errors of some 2D samples and the corresponding Voronoi cells, where bigger circle implies larger error

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Fig. 4

A 1D function and a metamodel through five initial samples

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Fig. 5

Samples generated by CV-Voronoi, ACE, and SFCVT for the 1D test case

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Fig. 6

The S values of CV-Voronoi, ACE, and SFCVT during the sampling process

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Fig. 7

Illustration of local exploitation and global exploration of the proposed approach

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Fig. 8

The trail of the additional 15 samples for the aforementioned 1D case by the proposed approach

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Fig. 9

Convergence histories (mean + SD) of CV-Voronoi, SFCVT, and ACE for a 1D test case

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Fig. 10

Two runs for function Peaks with (a) CV-Voronoi (37 samples) and (b) LOLA-Voronoi (47 samples), separately

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Fig. 11

Two runs for function Easom with (a) CV-Voronoi (83 samples) and (b) LOLA-Voronoi (126 samples), separately

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Fig. 12

Convergence histories (mean + SD) of different approaches for functions: (a) Peaks, (b) Shekel, and (c) Ackley10.

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Fig. 13

Required number of samples (mean + SD) to reach specified RMSE by CV-Voronoi with different numbers of initial samples for functions (a) Peaks, (b) Shekel, and (c) Hart6.

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