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 proceedingChapterpeer-review

1 Citation (Scopus)


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
Number of pages19
Publication statusPublished - 2009

Publication series

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


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