Comparison of the criteria for updating Kriging response surface models in multi-objective optimization

Koji Shimoyama, Koma Sato, Shinkyu Jeong, Shigeru Obayashi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

18 Citations (Scopus)

Abstract

This paper compares the criteria for updating the Kriging response surface models in multi-objective optimization: expected improvement (EI), expected hypervolume improvement (EHVI), estimation (EST), and those combination (EHVI+EST). EI has been conventionally used as the criterion considering the stochastic improvement of each objective function value individually, while EHVI has been recently proposed as the criterion considering the stochastic improvement of the front of non-dominated solutions in multi-objective optimization. EST is the value of each objective function, which is estimated non-stochastically by the Kriging model without considering its uncertainties. Numerical experiments were implemented in the welded beam design problem, and empirically showed that, in a non-constrained case, EHVI keeps a balance between accurate and wide search for non-dominated solutions on the Kriging models in multi-objective optimization. In addition, the present experiments suggested future investigation into the techniques for handling uncertain constraints to enhance the capability of EHVI in a constrained case.

Original languageEnglish
Title of host publication2012 IEEE Congress on Evolutionary Computation, CEC 2012
DOIs
Publication statusPublished - 2012
Event2012 IEEE Congress on Evolutionary Computation, CEC 2012 - Brisbane, QLD, Australia
Duration: 10 Jun 201215 Jun 2012

Publication series

Name2012 IEEE Congress on Evolutionary Computation, CEC 2012

Conference

Conference2012 IEEE Congress on Evolutionary Computation, CEC 2012
Country/TerritoryAustralia
CityBrisbane, QLD
Period10/06/1215/06/12

Keywords

  • Kriging response surface model
  • additional sample
  • expected hypervolume improvement
  • expected improvement
  • function estimation
  • multi-objective optimization

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