Optimal Design of a Pin-Fin Heat Sink Using a Surrogate-Assisted Multiobjective Evolutionary Algorithm
In this work, performance enhancement of a multiobjective evolutionary algorithm (MOEA) by integrating a surrogate model to the design process is presented. The MOEA used in this work is multiobjective population-based incremental learning (PBIL). The bi-objective design problem of a pin-fin heat sink (PFHS) is posed to minimize junction temperature and fan pumping power while meeting design constraints. A Kriging (KRG) model is used for improving the performance of PBIL. The training points for constructing a surrogate KRG model are sampled by means of a Latin hypercube sampling (LHS) technique. It is shown that hybridization of PBIL and KRG can enhance the search performance of PBIL.
S. Kanyakam and S. Bureerat, "Optimal Design of a Pin-Fin Heat Sink Using a Surrogate-Assisted Multiobjective Evolutionary Algorithm", Advanced Materials Research, Vols. 308-310, pp. 1122-1128, 2011