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

Resultado de la investigación: Contribución a los tipos de informe/libroContribución a la conferenciarevisión 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 originalInglés
Título de la publicación alojada2016 IEEE Congress on Evolutionary Computation, CEC 2016
EditorialInstitute of Electrical and Electronics Engineers Inc.
Páginas4662-4666
Número de páginas5
ISBN (versión digital)9781509006229
DOI
EstadoPublicada - 14 nov 2016
Evento2016 IEEE Congress on Evolutionary Computation, CEC 2016 - Vancouver, Canadá
Duración: 24 jul 201629 jul 2016

Otros

Otros2016 IEEE Congress on Evolutionary Computation, CEC 2016
País/TerritorioCanadá
CiudadVancouver
Período24/07/1629/07/16

Áreas temáticas de ASJC Scopus

  • Inteligencia artificial
  • Modelización y simulación
  • Informática aplicada
  • Control y optimización

Huella

Profundice en los temas de investigación de 'A comparative study of differential evolution algorithms for parameter fitting procedures'. En conjunto forman una huella única.

Citar esto