Parameter identification of an E. coli cultivation process model using hybrid metaheuristics Online publication date: Fri, 25-Jul-2014
by Olympia Roeva; Stefka Fidanova
International Journal of Metaheuristics (IJMHEUR), Vol. 3, No. 2, 2014
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.
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