A comparative study of differential evolution algorithms for parameter fitting procedures

Carlos E. Torres-Cerna; Alma Y. Alanis; Ignacio Poblete-Castro; Marta Bermejo-Jambrina; Esteban A. Hernandez-Vargas

doi:10.1109/CEC.2016.7744385

A comparative study of differential evolution algorithms for parameter fitting procedures

Carlos E. Torres-Cerna, Alma Y. Alanis, Ignacio Poblete-Castro, Marta Bermejo-Jambrina, Esteban A. Hernandez-Vargas

Facultad de Ciencias de la Vida

Resultado de la investigación: Contribución a los tipos de informe/libro › Contribución a la conferencia › revisión exhaustiva

7 Citas (Scopus)

Resumen

Parameter fitting consists on the estimation of model parameters using experimental data from the studied process, which can be considered as a nonlinear optimization problem. In this sense, evolutionary computation has shown its great capability to solve multimodal nonlinear optimization problems. This paper compares different variants of the Differential Evolution (DE) algorithm to minimize the residual sum of squares between the outcome of the mathematical model and experimental data. To compare the different variants of the DE algorithm, a biopolymer production model is considered. Simulations results suggest a trend for the best fit using the DE/best/ variants. However, the DE/rand/ variant provides more stable results respect to the average and standard deviation of different trials. Finally, the biopolymer production problem is discussed.

Idioma original	Inglés
Título de la publicación alojada	2016 IEEE Congress on Evolutionary Computation, CEC 2016
Editorial	Institute of Electrical and Electronics Engineers Inc.
Páginas	4662-4666
Número de páginas	5
ISBN (versión digital)	9781509006229
DOI	https://doi.org/10.1109/CEC.2016.7744385
Estado	Publicada - 14 nov 2016
Evento	2016 IEEE Congress on Evolutionary Computation, CEC 2016 - Vancouver, Canadá Duración: 24 jul 2016 → 29 jul 2016

Otros

Otros	2016 IEEE Congress on Evolutionary Computation, CEC 2016
País/Territorio	Canadá
Ciudad	Vancouver
Período	24/07/16 → 29/07/16

Áreas temáticas de ASJC Scopus

Inteligencia artificial
Modelización y simulación
Informática aplicada
Control y optimización

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10.1109/CEC.2016.7744385

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Torres-Cerna, C. E., Alanis, A. Y., Poblete-Castro, I., Bermejo-Jambrina, M., & Hernandez-Vargas, E. A. (2016). A comparative study of differential evolution algorithms for parameter fitting procedures. En 2016 IEEE Congress on Evolutionary Computation, CEC 2016 (pp. 4662-4666). [7744385] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CEC.2016.7744385

@inproceedings{1e74bac4f68a43a0bdff220762b0b1b9,

title = "A comparative study of differential evolution algorithms for parameter fitting procedures",

abstract = "Parameter fitting consists on the estimation of model parameters using experimental data from the studied process, which can be considered as a nonlinear optimization problem. In this sense, evolutionary computation has shown its great capability to solve multimodal nonlinear optimization problems. This paper compares different variants of the Differential Evolution (DE) algorithm to minimize the residual sum of squares between the outcome of the mathematical model and experimental data. To compare the different variants of the DE algorithm, a biopolymer production model is considered. Simulations results suggest a trend for the best fit using the DE/best/ variants. However, the DE/rand/ variant provides more stable results respect to the average and standard deviation of different trials. Finally, the biopolymer production problem is discussed.",

keywords = "Bioprocesses, Differential Evolution Algorithm, Parameter Estimation",

author = "Torres-Cerna, {Carlos E.} and Alanis, {Alma Y.} and Ignacio Poblete-Castro and Marta Bermejo-Jambrina and Hernandez-Vargas, {Esteban A.}",

year = "2016",

month = nov,

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doi = "10.1109/CEC.2016.7744385",

language = "English",

pages = "4662--4666",

booktitle = "2016 IEEE Congress on Evolutionary Computation, CEC 2016",

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Torres-Cerna, CE, Alanis, AY, Poblete-Castro, I, Bermejo-Jambrina, M & Hernandez-Vargas, EA 2016, A comparative study of differential evolution algorithms for parameter fitting procedures. En 2016 IEEE Congress on Evolutionary Computation, CEC 2016., 7744385, Institute of Electrical and Electronics Engineers Inc., pp. 4662-4666, 2016 IEEE Congress on Evolutionary Computation, CEC 2016, Vancouver, Canadá, 24/07/16. https://doi.org/10.1109/CEC.2016.7744385

A comparative study of differential evolution algorithms for parameter fitting procedures. / Torres-Cerna, Carlos E.; Alanis, Alma Y.; Poblete-Castro, Ignacio; Bermejo-Jambrina, Marta; Hernandez-Vargas, Esteban A.

2016 IEEE Congress on Evolutionary Computation, CEC 2016. Institute of Electrical and Electronics Engineers Inc., 2016. p. 4662-4666 7744385.

Resultado de la investigación: Contribución a los tipos de informe/libro › Contribución a la conferencia › revisión exhaustiva

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AU - Alanis, Alma Y.

AU - Poblete-Castro, Ignacio

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AU - Hernandez-Vargas, Esteban A.

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N2 - Parameter fitting consists on the estimation of model parameters using experimental data from the studied process, which can be considered as a nonlinear optimization problem. In this sense, evolutionary computation has shown its great capability to solve multimodal nonlinear optimization problems. This paper compares different variants of the Differential Evolution (DE) algorithm to minimize the residual sum of squares between the outcome of the mathematical model and experimental data. To compare the different variants of the DE algorithm, a biopolymer production model is considered. Simulations results suggest a trend for the best fit using the DE/best/ variants. However, the DE/rand/ variant provides more stable results respect to the average and standard deviation of different trials. Finally, the biopolymer production problem is discussed.

AB - Parameter fitting consists on the estimation of model parameters using experimental data from the studied process, which can be considered as a nonlinear optimization problem. In this sense, evolutionary computation has shown its great capability to solve multimodal nonlinear optimization problems. This paper compares different variants of the Differential Evolution (DE) algorithm to minimize the residual sum of squares between the outcome of the mathematical model and experimental data. To compare the different variants of the DE algorithm, a biopolymer production model is considered. Simulations results suggest a trend for the best fit using the DE/best/ variants. However, the DE/rand/ variant provides more stable results respect to the average and standard deviation of different trials. Finally, the biopolymer production problem is discussed.

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A comparative study of differential evolution algorithms for parameter fitting procedures

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