A comparative study of differential evolution algorithms for parameter fitting procedures
Carlos E. Torres-Cerna, Alma Y. Alanís, Ignacio Poblete‐Castro, Marta Bermejo‐Jambrina, Esteban Abelardo Hernandez-Vargas · 2016
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.