Updating kriging surrogate models based on the hypervolume indicator in multi-objective optimization

Koji Shimoyama, Koma Sato, Shinkyu Jeong, Shigeru Obayashi

Research output: Contribution to journalArticlepeer-review

29 Citations (Scopus)

Abstract

This paper presents a comparison of the criteria for updating the Kriging surrogate models in multi-objective optimization: expected improvement (EI), expected hypervolume improvement (EHVI), estimation (EST), and those in combination (EHVI + EST). EI has been conventionally used as the criterion considering the stochastic improvement of each objective function value individually, while EHVI has recently been proposed as the criterion considering the stochastic improvement of the front of nondominated solutions in multi-objective optimization. EST is the value of each objective function estimated nonstochastically by the Kriging model without considering its uncertainties. Numerical experiments were implemented in the welded beam design problem, and empirically showed that, in an unconstrained case, EHVI maintains a balance between accuracy, spread, and uniformity in nondominated solutions for Kriging-model-based multiobjective optimization. In addition, the present experiments suggested future investigation into techniques for handling constraints with uncertainties to enhance the capability of EHVI in constrained cases.

Original languageEnglish
Article number094503
JournalJournal of Mechanical Design, Transactions of the ASME
Volume135
Issue number9
DOIs
Publication statusPublished - 2013 Sep

ASJC Scopus subject areas

  • Mechanics of Materials
  • Mechanical Engineering
  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design

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