Published

2017-05-01

Response surface methodology for estimating missing values in a pareto genetic algorithm used in parameter design

Metodología de superficie de respuesta para estimar valores faltantes en un algoritmo genético de pareto usado en diseño de parámetros

DOI:

https://doi.org/10.15446/ing.investig.v37n2.57152

Keywords:

Robust design, parameter design, pareto genetic algorithm, response surface methodology (en)
Diseño robusto, diseño de parámetros, algoritmo genético de pareto, metodología de superficie de respuesta. (es)

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Authors

  • Enrique Canessa Universidad Adolfo Ibáñez
  • Sergio Chaigneau Universidad Adolfo Ibáñez
We present an improved Pareto Genetic Algorithm (PGA), which finds solutions to problems of robust design in multi-response systems with 4 responses and as many as 10 control and 5 noise factors. Because some response values might not have been obtained in the robust design experiment and are needed in the search process, the PGA uses Response Surface Methodology (RSM) to estimate them. Not only the PGA delivered solutions that adequately adjusted the response means to their target values, and with low variability, but also found more Pareto efficient solutions than a previous version of the PGA. This improvement makes it easier to find solutions that meet the trade-off among variance reduction, mean adjustment and economic considerations. Furthermore, RSM allows estimating outputs’ means and variances in highly non-linear systems, making the new PGA appropriate for such systems.
En este artículo se presenta un Algoritmo Genético de Pareto (AGP) mejorado que encuentra soluciones a problemas de diseño robusto en sistemas multi-respuesta con 4 respuestas y hasta 10 factores de control y 5 de ruido. Ya que algunas respuestas podrían no haber sido obtenidas en el experimento de diseño robusto y se necesitan en el proceso de búsqueda, el AGP usa metodología de superficie de respuesta (MSR) para estimarlas. El AGP no solo entregó soluciones que ajustan adecuadamente la media de las respuestas a sus valores meta y con poca variabilidad, sino que también encontró más soluciones Pareto eficientes que una versión previa del AGP. Esta mejora facilita encontrar soluciones que alcancen el balance entre reducción de variabilidad, ajuste de media y consideraciones económicas. Además, la MSR permite estimar las medias y varianzas de las respuestas de sistemas altamente no lineales, haciendo apropiado el uso del AGP en dichos sistemas.

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Canessa, E., & Chaigneau, S. (2017). Response surface methodology for estimating missing values in a pareto genetic algorithm used in parameter design. Ingeniería E Investigación, 37(2), 89-98. https://doi.org/10.15446/ing.investig.v37n2.57152
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Copyright (c) 2017 Enrique Canessa, Sergio Chaigneau

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