A new efficient and useful robust optimization approach - design for multi-objective six sigma

An efficient and useful robust optimization approach, design for multi-objective six sigma (DFMOSS), has been developed. The DFMOSS couples the ideas of design for six sigma (DFSS) and multi-objective genetic algorithm (MOGA) to solve drawbacks of DFSS; DFMOSS obtains trade-off solutions between opt...

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Bibliographic Details
Published in2005 IEEE Congress on Evolutionary Computation Vol. 1; pp. 950 - 957 Vol.1
Main Authors Shimoyama, K., Oyama, A., Fujii, K.
Format Conference Proceeding
LanguageEnglish
Published IEEE 2005
Subjects
Algorithm design and analysis
Design engineering
Design optimization
Genetic algorithms
Process design
Robust stability
Robustness
Six sigma
Uncertainty
Welding
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Summary:An efficient and useful robust optimization approach, design for multi-objective six sigma (DFMOSS), has been developed. The DFMOSS couples the ideas of design for six sigma (DFSS) and multi-objective genetic algorithm (MOGA) to solve drawbacks of DFSS; DFMOSS obtains trade-off solutions between optimality and robustness in one optimization. In addition, it does not need careful parameter tuning. Robust optimizations of a test function and welded beam design problem demonstrated that DFMOSS is more effective and more useful than DFSS
ISBN:0780393635
9780780393639
ISSN:1089-778X
1941-0026
DOI:10.1109/CEC.2005.1554785