Comparative study between the generalized reduced gradient and genetic algorithm in multiple response optimization

Authors

  • Fabrício Maciel Gomes Departamento de Engenharia Química Escola de Engenharia de Lorena Universidade de São Paulo http://orcid.org/0000-0001-7694-9835
  • Félix Monteiro Pereira Departamento de Engenharia Química Escola de Engenharia de Lorena Universidade de São Paulo http://orcid.org/0000-0002-5749-4107
  • Fernando Augusto Silva Marins Departamento de Produção Faculdade de Engenharia de Guaratinguetá Universidade Estadual Júlio Mesquita Filho http://orcid.org/0000-0001-6510-9187
  • Messias Borges Silva Departamento de Engenharia Química Escola de Engenharia de Lorena Universidade de São Paulo http://orcid.org/0000-0002-8656-0791

DOI:

https://doi.org/10.14488/1676-1901.v17i2.2566

Keywords:

Optimization. Multiple Response. Genetic Algorithm. GRG.

Abstract

 In this paper we present a comparative study between the Generalized Reduced Gradient (GRG) and Genetic Algorithm (GA) methods to optimize multiple-response processes. Results from experiment design were used to compose the objective function to be minimized. The case studies in this work were selected from literature. A Microsoft Excel spreadsheet was used for parameters optimization using GRG, and the Scilab software was used to GA. Ten replicates were performed and the mean of the results was obtained. To assess the methods was used performance measures based on the mean percentage error. From the performance measures used, the AG showed better results compared to the GRG, indicating that the AG can generate better responses than GRG.

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Author Biographies

Fabrício Maciel Gomes, Departamento de Engenharia Química Escola de Engenharia de Lorena Universidade de São Paulo

Engenheiro Industrial Químico

Mestre em Engenharia Química

Doutor em Engenharia de Produção

Professor do Departamento de Engenharia Química

Área: Modelagem, Simulação e Otimização de Processos

Félix Monteiro Pereira, Departamento de Engenharia Química Escola de Engenharia de Lorena Universidade de São Paulo

Engenheiro Industrial Químico

Mestre em Biotecnologia Industrial

Doutor em Biotecnologia Industrial

Professor do Departamento de Engenharia Química

Área: Modelagem, Simulação e Otimização de Processos

Fernando Augusto Silva Marins, Departamento de Produção Faculdade de Engenharia de Guaratinguetá Universidade Estadual Júlio Mesquita Filho

Engenheiro Mecânico

Mestre em Ciências

Doutor em Engenharia Elétrica

Professor Titular do Departamento de Produção

Área: Modelagem e Pesquisa Operacional

Messias Borges Silva, Departamento de Engenharia Química Escola de Engenharia de Lorena Universidade de São Paulo

Engenheiro Industrial Químico

Mestre em Engenharia Mecânica

Doutor em Engenharia Química

Professor do Departamento de Engenharia Química

Área: Planejamento de Experimentos e Otimização de Processos

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Published

2017-06-14

How to Cite

Gomes, F. M., Pereira, F. M., Marins, F. A. S., & Silva, M. B. (2017). Comparative study between the generalized reduced gradient and genetic algorithm in multiple response optimization. Revista Produção Online, 17(2), 592–619. https://doi.org/10.14488/1676-1901.v17i2.2566

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Papers