Abstract—Surrogate models can be used to replace expensive
computer simulations for the purposes of optimization. In this
paper, we propose an optimization approach based on artificial
neural network (ANN) surrogate models and infill sampling
criteria (ISC) strategy to evaluate design variables. The
criterion for infill sample selection is a function which aims at
identify design that offer potential improvement. We employ
four widely used analytical benchmark problems to test the
proposed approach. Our results show that a more accurate
surrogate model obtained with fewer points is obtained when
one includes the infill sample criterion to an ANN-based
optimization.
Index Terms—Surrogate model, design variables, artificial
neural network, infill sampling criteria, optimization,
benchmark function.
Han Li, Leonardo Gutierrez, Rafael Batres, and Masakazu Kobayashi are
with the Department of Mechanical Engineering, Toyohashi University of
Technology, Toyohashi, Aichi, Japan (e-mail: {lihan,
Leonardo}@ise.me.tut.ac.jp; m-kobayashi@me.tut.ac.jp; rbp@tut.jp).
Osamu Kuwazuru is with the Department Nuclear Power & Energy
Safety Engineering, University of Fukui, Fukui 910-8507, Japan (e-mail:
kuwa@u-fukui.ac.jp).
Hiroyuki Toda is with the Department of Mechanical Engineering,
Kyushu University, Kyushu, Japan (e-mail: toda@mech.kyushu-u.ac.jp).
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Cite:Han Li, Leonardo Gutierrez, Masakazu Kobayashi, Osamu Kuwazuru, Hiroyuki Toda, and Rafael Batres, "A Numerical Evaluation of an Infill Sampling Criterion in Artificial Neural Network-Based Optimization," International Journal of Computer Theory and Engineering vol. 6, no. 3, pp. 272-277, 2014.
