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- Title
Comparative Performance of Surrogate-Assisted MOEAs for Geometrical Design of Pin-Fin Heat Sinks.
- Authors
Kanyakam, Siwadol; Bureerat, Sujin
- 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.
- Subjects
COMPARATIVE studies; PERFORMANCE evaluation; SYSTEMS design; HEAT sinks (Electronics); EVOLUTIONARY algorithms; RESPONSE surfaces (Statistics); GAUSSIAN processes
- Publication
Journal of Applied Mathematics, 2012, p1
- ISSN
1110-757X
- Publication type
Article
- DOI
10.1155/2012/534783