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The surrogate\u2010assisted MOEAs are achieved by integrating multiobjective population\u2010based incremental learning (PBIL) with a quadratic response surface model (QRS), a radial\u2010basis 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.<\/jats:p>","DOI":"10.1155\/2012\/534783","type":"journal-article","created":{"date-parts":[[2012,5,13]],"date-time":"2012-05-13T21:02:25Z","timestamp":1336942945000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Comparative Performance of Surrogate\u2010Assisted MOEAs for Geometrical Design of Pin\u2010Fin Heat Sinks"],"prefix":"10.1155","volume":"2012","author":[{"given":"Siwadol","family":"Kanyakam","sequence":"first","affiliation":[]},{"given":"Sujin","family":"Bureerat","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2012,5,13]]},"reference":[{"key":"e_1_2_8_1_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCAPT.2008.916799"},{"key":"e_1_2_8_2_2","doi-asserted-by":"publisher","DOI":"10.1080\/03052150903143935"},{"key":"e_1_2_8_3_2","unstructured":"KanyakamS.andBureeratS. 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