An approach for multi-objective robust optimization assisted by response surface approximation and visual data-mining

Koji Shimoyama, Jin Ne Lim, Shinkyu Jeong, Shigeru Obayashi, Masataka Koishi

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

4 Citations (Scopus)

Abstract

A new approach for multi-objective robust design optimization has been proposed and applied to a real-world design problem with a large number of objective functions. The present approach is assisted by response surface approximation and visual data-mining, which results in two major gains regarding computational time and data interpretation. The Kriging model for response surface approximation can realize accurate predictions of robustness measures, and dramatically reduces the computational time for objective function evaluation. In addition, the use of self-organizing maps as a datamining technique allows visualization of complicated design information between optimality and robustness of design in a comprehensible two-dimensional form. Therefore, the extraction and interpretation of trade-off relations between optimality and robustness of design, and also the location of sweet-spots in the design space, can be performed in a comprehensive manner.

Original languageEnglish
Title of host publication2007 IEEE Congress on Evolutionary Computation, CEC 2007
Pages2413-2420
Number of pages8
DOIs
Publication statusPublished - 2007 Dec 1
Event2007 IEEE Congress on Evolutionary Computation, CEC 2007 - , Singapore
Duration: 2007 Sep 252007 Sep 28

Publication series

Name2007 IEEE Congress on Evolutionary Computation, CEC 2007

Other

Other2007 IEEE Congress on Evolutionary Computation, CEC 2007
CountrySingapore
Period07/9/2507/9/28

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software
  • Theoretical Computer Science

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