Abstract
The present work offers a novel approach to parameter identification of an E. coli cultivation process model, using a hybrid of two metaheuristics, namely Ant Colony Optimization (ACO) and Genetic Algorithms (GAs). Our basic idea is to generate initial solutions by the ACO method, and then serve these solutions to the GA as its initial population of individuals. Thus, the GA will start with a population, which is not randomly generated, as in the general case, but one rather closer to an optimal solution. The motivation behind this hybridization is to combine the benefits of both approaches, aimed at achieving commensurate calculations precision with less computation resources, in terms of time and memory. The proposed method is approbated with the estimation of the parameters of a real E. coli fed-batch cultivation process model. The presented results are affirmative of our goal to yield better performance of the hybrid algorithm: almost twice less computational time and approximately five times smaller populations needed, compared to both ACO and GAs, as taken separately.