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 proceedingChapter

1 Citation (Scopus)

Abstract

A new approach for multi-objective robust design optimization was 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, and resulted in two major gains regarding computational time and data interpretation. The Kriging model for response surface approximation can markedly reduce the computational time for predictions of robustness. In addition, the use of self-organizing maps as a data- mining technique allows visualization of complicated design information between optimality and robustness 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 publicationMulti-Objective Memetic Algorithms
EditorsChi-Keong Goh, Kay Chen Tan, Yew-Soon Ong
Pages133-151
Number of pages19
DOIs
Publication statusPublished - 2009 Jan 12

Publication series

NameStudies in Computational Intelligence
Volume171
ISSN (Print)1860-949X

ASJC Scopus subject areas

  • Artificial Intelligence

Fingerprint Dive into the research topics of 'Multi-objective robust optimization assisted by response surface approximation and visual data-mining'. Together they form a unique fingerprint.

Cite this