Robust Design in Multiobjective Systems using Taguchi’s Parameter Design Approach and a Pareto Genetic Algorithm


  • Enrique Canessa Adolfo Ibanez University
  • Gabriel Bielenberg Adolfo Ibanez University
  • Héctor Allende Adolfo Ibanez University



Pareto genetic algorithms, multiobjective evolutionary algorithm, parameter design


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.

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Author Biographies

Enrique Canessa, Adolfo Ibanez University

Faculty of Engineering and Sciences.

Gabriel Bielenberg, Adolfo Ibanez University

Faculty of Engineering and Sciences.

Héctor Allende , Adolfo Ibanez University

Department of Computer Science, Faculty of Engineering and Sciences. teacher.


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How to Cite

Canessa, E., Bielenberg, G., & Allende , H. (2014). Robust Design in Multiobjective Systems using Taguchi’s Parameter Design Approach and a Pareto Genetic Algorithm. Revista Facultad De Ingeniería Universidad De Antioquia, (72), 73–86.

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