Comparative Performance of Surrogate-Assisted MOEAs for Geometrical Design of Pin-Fin Heat Sinks

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.