Effects of the number of design variables on performances in Kriging-model-based many-objective optimization

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

The effects of the number of design variables on the optimization performances in Kriging-model-based manyobjective optimizations, which use expected hypervolume improvement (EHVI), expected improvement (EI), and estimation (EST) as the criteria for updating the Kriging model, are investigated based on four independent performance metrics in this paper. Numerical experiments are conducted in 3 to 15-objective DTLZ1 and DTLZ7 problems. The results indicate that the advantages of EHVI over EI and EST are more obvious when the number of design variables increases, and EHVI is more suitable for the problems with a large number of design variables. In addition, the comparison results show that, EHVI obtains faster IGD reduction than EI and EST in most test problems. The advantage of EHVI over EI and EST is mainly shown on the convergence performance. The spread performance is better in both EHVI and EI considering estimation errors than EST without considering estimation errors. However, the uniformity of EHVI is weak, especially for the problems with a large number of objectives.

Original languageEnglish
Title of host publication2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1901-1908
Number of pages8
ISBN (Electronic)9781479974924
DOIs
Publication statusPublished - 2015 Sep 10
EventIEEE Congress on Evolutionary Computation, CEC 2015 - Sendai, Japan
Duration: 2015 May 252015 May 28

Publication series

Name2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings

Other

OtherIEEE Congress on Evolutionary Computation, CEC 2015
CountryJapan
CitySendai
Period15/5/2515/5/28

Keywords

  • Kriging model
  • expected hypervolume improvement (EHVI)
  • many-objective optimization

ASJC Scopus subject areas

  • Computer Science Applications
  • Computational Mathematics

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    Luo, C., Shimoyama, K., & Obayashi, S. (2015). Effects of the number of design variables on performances in Kriging-model-based many-objective optimization. In 2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings (pp. 1901-1908). [7257118] (2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CEC.2015.7257118