Authors: Olympia Roeva; Stefka Fidanova
Addresses: Department of Bioinformatics and Mathematical Modelling, Institute of Biophysics and Biomedical Engineering, BAS, Sofia, Bulgaria ' Department on Parallel Algorithms, Institute of Information and Communication Technology, BAS, Sofia, Bulgaria
Abstract: The present work offers a novel approach to parameter identification of an E. coli cultivation process model, using hybrids of three metaheuristics - ant colony optimisation (ACO), firefly algorithm (FA) and genetic algorithm (GA). The motivation behind this hybridisation is to combine the benefits of these approaches, aimed at achieving commensurate calculations precision with less computation resources, in terms of time and memory. The proposed hybrids are approbated with the estimation of the parameters of a real E. coli cultivation process model. In the considered non-linear mathematical model three parameters are estimated, namely, maximum specific growth rate, saturation constant and yield coefficient. Based on the numerical and simulation results, it is shown that the models obtained by the proposed hybrid algorithms are competitive with standard ACO, FA and GA. The hybrids show two advantages - have much less running time and require much less memory compared to standard ACO, FA and GA.
Keywords: parameter identification; E. coli; ant colony optimisation; ACO; genetic algorithms; firefly algorithm; cultivation process; hybrid metaheuristics; Escherichia coli.
International Journal of Metaheuristics, 2014 Vol.3 No.2, pp.133 - 148
Received: 11 Oct 2013
Accepted: 28 Mar 2014
Published online: 03 Jul 2014 *