Kriging model based many-objective optimization with efficient calculation of expected hypervolume improvement

Chang Luo, Koji Shimoyama, Shigeru Obayashi

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

18 Citations (Scopus)

Abstract

The many-objective optimization performance of using expected hypervolume improvement (EHVI) as the updating criterion of the Kriging surrogate model is investigated, and compared with those of using expected improvement (EI) and estimation (EST) updating criteria in this paper. An exact algorithm to calculate hypervolume is used for the problems with less than six objectives. On the other hand, in order to improve the efficiency of hypervolume calculation, an approximate algorithm to calculate hypervolume based on Monte Carlo sampling is adopted for the problems with more objectives. Numerical experiments are conducted in 3 to 12-objective DTLZ1, DTLZ2, DTLZ3 and DTLZ4 problems. The results show that, in DTLZ3 problem, EHVI always obtains better convergence and diversity performances than EI and EST for any number of objectives. In DTLZ2 and DTLZ4 problems, the advantage of EHVI is shown gradually as the number of objectives increases. The present results suggest that EHVI will be a highly competitive updating criterion for the many-objective optimization with the Kriging model.

Original languageEnglish
Title of host publicationProceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1187-1194
Number of pages8
ISBN (Electronic)9781479914883
DOIs
Publication statusPublished - 2014 Sept 16
Event2014 IEEE Congress on Evolutionary Computation, CEC 2014 - Beijing, China
Duration: 2014 Jul 62014 Jul 11

Publication series

NameProceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014

Conference

Conference2014 IEEE Congress on Evolutionary Computation, CEC 2014
Country/TerritoryChina
CityBeijing
Period14/7/614/7/11

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