Kriging-model-based multi-objective robust optimization and trade-off-rule mining using association rule with aspiration vector

Kazuyuki Sugimura, Shinkyu Jeong, Shigeru Obayashi, Takeshi Kimura

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

13 Citations (Scopus)

Abstract

A new design method called MORDE (multi-objective robust design exploration), which conducts both a multi-objective robust optimization and data mining for analyzing trade-offs, is proposed. For the robust optimization, probabilistic representation of design parameters is incorporated into a multi-objective genetic algorithm. The means and standard deviations of responses of evaluation functions to uncertainties in design variables are evaluated by descriptive Latin hypercube sampling using Kriging surrogate models. To extract trade-off control rules further, a new approach, which combines the association rule with an "aspiration vector, " is proposed. MORDE is then applied to an industrial design problem concerning a centrifugal fan. Taking dimensional uncertainty into account, MORDE then optimized the means and standard deviations of the resulting distributions of fan efficiency and turbulent noise level. The advantages of MORDE over traditional approaches are shown to be the diversity of the solutions and the quantitative controllability of the trade-off balance among multiple objective functions.

Original languageEnglish
Title of host publication2009 IEEE Congress on Evolutionary Computation, CEC 2009
Pages522-529
Number of pages8
DOIs
Publication statusPublished - 2009 Nov 25
Event2009 IEEE Congress on Evolutionary Computation, CEC 2009 - Trondheim, Norway
Duration: 2009 May 182009 May 21

Publication series

Name2009 IEEE Congress on Evolutionary Computation, CEC 2009

Other

Other2009 IEEE Congress on Evolutionary Computation, CEC 2009
CountryNorway
CityTrondheim
Period09/5/1809/5/21

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Theoretical Computer Science

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