You are required to read and agree to the below before accessing a full-text version of an article in the IDE article repository.

The full-text document you are about to access is subject to national and international copyright laws. In most cases (but not necessarily all) the consequence is that personal use is allowed given that the copyright owner is duly acknowledged and respected. All other use (typically) require an explicit permission (often in writing) by the copyright owner.

For the reports in this repository we specifically note that

  • the use of articles under IEEE copyright is governed by the IEEE copyright policy (available at
  • the use of articles under ACM copyright is governed by the ACM copyright policy (available at
  • technical reports and other articles issued by M‰lardalen University is free for personal use. For other use, the explicit consent of the authors is required
  • in other cases, please contact the copyright owner for detailed information

By accepting I agree to acknowledge and respect the rights of the copyright owner of the document I am about to access.

If you are in doubt, feel free to contact

Adaptive Differential Evolution Supports Automatic Model Calibration in Furnace Optimized Control System

Research group:

Publication Type:

Book chapter


Computational Intelligence




Model calibration represents the task of estimating the parameters of a process model to obtain a good match between observed and simulated behaviours. This can be considered as an optimization problem to search for model parameters that minimize the discrepancy between the model outputs and the corresponding features from the historical empirical data. This chapter investigates the use of Differential Evolution (DE), a competitive class of evolutionary algorithms, to solve calibration problems for nonlinear process models. The merits of DE include simple and compact structure, easy implementation, as well as high convergence speed. However, the good performance of DE relies on proper setting of its running parameters such as scaling factor and crossover probability, which are problem dependent and which can even vary in the different stages of the search. To mitigate this issue, we propose a new adaptive DE algorithm that dynamically adjusts its running parameters during its execution. The core of this new algorithm is the incorporated greedy local search, which is conducted in successive learning periods to continuously locate better parameter assignments in the optimization process. In case studies, we have applied our proposed adaptive DE algorithm for model calibration in a Furnace Optimized Control System. The statistical analysis of experimental results demonstrate that the proposed DE algorithm can support the creation of process models that are more accurate than those produced by standard DE.


@incollection{Leon Ortiz4528,
author = {Miguel Leon Ortiz and Magnus Evestedt and Ning Xiong},
title = {Adaptive Differential Evolution Supports Automatic Model Calibration in Furnace Optimized Control System},
editor = {Agostinho Rosa, Juan Julian Merelo, Ant{\'o}nio Dourado, Jos{\'e} M. Cadenas, Kurosh Madani, Ant{\'o}nio Ruano and Joaquim Filipe},
volume = {669},
month = {January},
year = {2017},
booktitle = {Computational Intelligence},
publisher = {Springer},
url = {}