METAHEURISTIC TECHNIQUES FOR OPTIMIZATION OF AN E. COLI CULTIVATION MODEL

In this paper two metaheuristics: Ant Colony Optimization (AGO) and Genetic Algorithms (GA), were compared for parameter identification of an E. coli fed-batch cultivation process model. A system of ordinary differential equations was used to model the biomass growth and substrate utilization. Parameter optimization was performed using a real experimental data set from an E. coli MC4110 fed-batch cultivation process. The GA and AGO adjustments were done based on several pre-tests on the optimization problem considered here. Two techniques were compared based on the obtained best', worst and average values for estimates and the objective function J. The results showed that the best value of the objective function J is achieved by ACO. At the same time, GA achieved better results for worst and average values. Analyzing the results, it could be concluded that both algorithms: AGO and GA, perform satisfactorily for the problem of parameter optimization of an E. coli fed-batch cultivation process model.