Title | Genetic Fuzzy Modelling of Li-Ion Batteries Through a Combination of Theta-DEA and Knowledge-Based Preference Ordering |
Publication Type | Conference Paper |
Year of Publication | 2016 |
Authors | Echevarría, Yuviny, Sánchez Luciano, and Blanco Cecilio |
Editor | Luaces, Oscar, Gámez José A., Barrenechea Edurne, Troncoso Alicia, Galar Mikel, Quintián Héctor, and Corchado Emilio |
Conference Name | Advances in Artificial Intelligence |
Pagination | 310–320 |
Publisher | Springer International Publishing |
Conference Location | Cham |
ISBN Number | 978-3-319-44636-3 |
Abstract | Learning semi-physical fuzzy models of rechargeable Li-Ion batteries from data involves solving a complex multicriteria optimization task where the accuracies of the approximations of the different observable variables are balanced. The fitness function of this problem depends on the recursive evaluation of a set of differential equations, where fuzzy rule-based systems are embedded as nonlinear blocks. Evaluating this function is a time consuming process, thus algorithms that efficiently promote diversity and hence demand a low number of evaluations of the fitness function are preferred. In this paper, a comparison is carried out between some recent genetic algorithms, whose performances are assessed in this particular modelling problem. It is concluded that the combination of the recent {\$}{\$}{\backslash}theta {\$}{\$}-Dominance Evolutionary Algorithm ({\$}{\$}{\backslash}theta {\$}{\$}-DEA) with a Knowledge-based precedence operator, that improves the selection, is a sensible choice. Dominance relations between the Pareto fronts are assessed in terms of binary additive {\$}{\$}{\backslash}epsilon {\$}{\$}-quality indicators. |