Data mining of non-dominated solutions using proper orthogonal decomposition

Akira Oyama, Taku Nonomura, Kozo Fujii

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

1 Citation (Scopus)

Abstract

A new approach to extract useful design information from non-dominated solutions of real-world multiobjective optimization problems is proposed. The proposed approach enables an analysis of line, face, or volume data that Pareto-optimal solutions have such as flow field and stress distribution by decomposing the data into principal modes using proper orthogonal decomposition. Analysis of the shape and surface pressure data of the non-dominated solutions of an aerodynamic transonic airfoil shape optimization problem shows capability of the proposed approach for design knowledge extraction for real-world design optimization problems.

Original languageEnglish
Title of host publicationProceedings of the 11th Annual Genetic and Evolutionary Computation Conference, GECCO-2009
Pages1935-1936
Number of pages2
DOIs
Publication statusPublished - 2009 Dec 31
Externally publishedYes
Event11th Annual Genetic and Evolutionary Computation Conference, GECCO-2009 - Montreal, QC, Canada
Duration: 2009 Jul 82009 Jul 12

Publication series

NameProceedings of the 11th Annual Genetic and Evolutionary Computation Conference, GECCO-2009

Other

Other11th Annual Genetic and Evolutionary Computation Conference, GECCO-2009
CountryCanada
CityMontreal, QC
Period09/7/809/7/12

Keywords

  • Data mining
  • Multi-objective evolutionary algorithm
  • Real world application

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Theoretical Computer Science

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