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

Life Sciences School

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

9 Citations (Scopus)

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.

Original language	English
Title of host publication	2016 IEEE Congress on Evolutionary Computation, CEC 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	4662-4666
Number of pages	5
ISBN (Electronic)	9781509006229
DOIs	https://doi.org/10.1109/CEC.2016.7744385
Publication status	Published - 14 Nov 2016
Event	2016 IEEE Congress on Evolutionary Computation, CEC 2016 - Vancouver, Canada Duration: 24 Jul 2016 → 29 Jul 2016

Other

Other	2016 IEEE Congress on Evolutionary Computation, CEC 2016
Country/Territory	Canada
City	Vancouver
Period	24/07/16 → 29/07/16

Keywords

Bioprocesses
Differential Evolution Algorithm
Parameter Estimation

ASJC Scopus subject areas

Artificial Intelligence
Modelling and Simulation
Computer Science Applications
Control and Optimization

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10.1109/CEC.2016.7744385

Cite this

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. In 2016 IEEE Congress on Evolutionary Computation, CEC 2016 (pp. 4662-4666). Article 7744385 Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CEC.2016.7744385

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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.",

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

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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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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Keywords

ASJC Scopus subject areas

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