Robust design in multivariate systems using genetic algorithms
Allende
H
author
Bravo
D
author
Canessa
E
author
2010
English
This paper presents a methodology based oil genetic algorithms, which finds feasible and reasonably adequate Solutions to problems of robust design in multivariate systems. We use a genetic algorithm to determine the appropriate control factor levels for simultaneously optimizing all of the responses of the system, considering the noise factors which affect it. The algorithm is guided by a desirability function which works with only one fitness function although the system May have many responses. We validated the methodology using data obtained from a real system and also from a process simulator, considering univariate and multivariate systems. In all cases, the methodology delivered feasible solutions, which accomplished the goals of robust design: obtain responses very close to the target values of each of them, and with minimum variability. Regarding the adjustment of the mean of each response to the target value, the algorithm performed very well. However, only in some of the multivariate cases, the algorithm was able to significantly reduce the variability of the responses.
Robust design
Taguchi methods
Genetic algorithms
Desirability functions
WOS:000275327300008
exported from refbase (show.php?record=82), last updated on Thu, 01 Apr 2010 02:25:35 -0300
text
files/82_Allende_etal2010.pdf
10.1007/s11135-008-9201-z
Allende_etal2010
Quality & Quantity
Qual. Quant.
2010
Springer
continuing
periodical
academic journal
44
2
315
332
0033-5177