Efficient Global Optimization (EGO) for multi-objective problem and data mining

Shinkyu Jeong, Shigeru Obayashi

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

56 Citations (Scopus)

Abstract

In this study, a surrogate model is applied to multi-objective aerodynamic optimization design. For the balanced exploration and exploitation with the surrogate model, objective functions are converted to the Expected Improvements (EI) and these values are directly used as fitness values in the multi-objective optimization. Among the non-dominated solutions about EIs, additional sample points for the update of the Kriging model are selected. The present method is applied to a transonic airfoil design. In order to obtain the information about design space, two data mining techniques are applied to design results. One is Analysis of Variance (ANOVA) and the other is Self-Organizing Map (SOM).

Original languageEnglish
Title of host publication2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005. Proceedings
Pages2138-2145
Number of pages8
Publication statusPublished - 2005 Oct 31
Event2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005 - Edinburgh, Scotland, United Kingdom
Duration: 2005 Sep 22005 Sep 5

Publication series

Name2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005. Proceedings
Volume3

Other

Other2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005
CountryUnited Kingdom
CityEdinburgh, Scotland
Period05/9/205/9/5

ASJC Scopus subject areas

  • Engineering(all)

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  • Cite this

    Jeong, S., & Obayashi, S. (2005). Efficient Global Optimization (EGO) for multi-objective problem and data mining. In 2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005. Proceedings (pp. 2138-2145). (2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005. Proceedings; Vol. 3).