RT Journal Article
T1 Learning FCMs with multi-local and balanced memetic algorithms for forecasting industrial drying processes
A1 Salmerón Silvera, José Luis
A1 Ruiz Celma, A.
A1 Mena Nieto, Ángel Isidro
AB In this paper, we propose a Fuzzy Cognitive Map (FCM) learning approach with a multi-local search in balanced memetic algorithms for forecasting industrial drying processes. The first contribution of this paper is to propose a FCM model by an Evolutionary Algorithm (EA), but the resulted FCM model is improved by a multi-local and balanced local search algorithm. Memetic algorithms can be tuned with different local search strategies (CMA-ES, SW, SSW and Simplex) and the balance of the effort between global and local search. To do this, we applied the proposed approach to the forecasting of moisture loss in industrial drying process. The thermal drying process is a relevant one used in many industrial processes such as food industry, biofuels production, detergents and dyes in powder production, pharmaceutical industry, reprography applications, textile industries, and others. This research also shows that exploration of the search space is more relevant than finding local optima in the FCM models tested.
PB Elsevier
SN 0925-2312
YR 2017
FD 2017-04-05
LK http://hdl.handle.net/10272/19474
UL http://hdl.handle.net/10272/19474
LA eng
NO Salmeron, J. L., Ruiz-Celma, A., & Mena, A. (2017). Learning FCMs with multi-local and balanced memetic algorithms for forecasting industrial drying processes. Neurocomputing, 232, 52–57. https://doi.org/10.1016/j.neucom.2016.10.070
DS Repositorio Institucional de la Universidad de Huelva
RD 31 may 2026