Open Access
Translator Disclaimer
2012 Comparative Performance of Surrogate-Assisted MOEAs for Geometrical Design of Pin-Fin Heat Sinks
Siwadol Kanyakam, Sujin Bureerat
J. Appl. Math. 2012(SI05): 1-14 (2012). DOI: 10.1155/2012/534783

Abstract

This paper presents the comparative performance of several surrogate-assisted multiobjective evolutionary algorithms (MOEAs) for geometrical design of a pin-fin heat sink (PFHS). The surrogate-assisted MOEAs are achieved by integrating multiobjective population-based incremental learning (PBIL) with a quadratic response surface model (QRS), a radial-basis function (RBF) interpolation technique, and a Kriging (KRG) or Gaussian process model. The mixed integer/continuous multiobjective design problem of PFHS with the objective to minimise junction temperature and fan pumping power simultaneously is posed. The optimum results obtained from using the original multiobjective PBIL and the three versions of hybrid PBIL are compared. It is shown that the hybrid PBIL using KRG is the best performer. The hybrid PBILs require less number of function evaluations to surpass the original PBIL.

Citation

Download Citation

Siwadol Kanyakam. Sujin Bureerat. "Comparative Performance of Surrogate-Assisted MOEAs for Geometrical Design of Pin-Fin Heat Sinks." J. Appl. Math. 2012 (SI05) 1 - 14, 2012. https://doi.org/10.1155/2012/534783

Information

Published: 2012
First available in Project Euclid: 17 October 2012

Digital Object Identifier: 10.1155/2012/534783

Rights: Copyright © 2012 Hindawi

JOURNAL ARTICLE
14 PAGES


SHARE
Vol.2012 • No. SI05 • 2012
Back to Top