Robust design in multiobjective systems using Taguchi's Parameter design approach and a Pareto Genetic Algorithm

Enrique Canessa, Gabriel Bielenberg, Héctor Allende

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

We present a Pareto Genetic Algorithm (PGA), which finds the Pareto frontier of solutions to problems of robust design in multiobjective systems. The PGA was designed to be applied using Taguchi's Parameter Design method, which is the most frequently used approach by practitioners to executing robust design studies. We tested the PGA using data obtained from a real singleoutput system and from multiobjective process simulators with many control and noise factors. In all cases, the PGA delivered Pareto-optimal solutions that adequately achieved the objective of robust design. Additionally, the discussion of the results showed that having those Pareto solutions helps in the selection of the best ones to be implemented in the system under study, especially when the system has many control factors and responses.

Original languageEnglish
Pages (from-to)73-86
Number of pages14
JournalRevista Facultad de Ingenieria
Issue number72
StatePublished - 2014

Keywords

  • Multiobjective evolutionary algorithms
  • Parameter design
  • Pareto genetic algorithms

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