Kinetic model parameter estimation by hybrid differential evolution algorithm

The determination of the optimal model parameters for kinetic systems is a time consuming, iterative process [1]. In this paper, we presented a novel hybrid Differential Evolution (DE) algorithm for solving kinetic parameter estimation problems based on the Differential Evolution technique together with a local search strategy. By combining the merits of DE with Gauss-Newton method, the proposed hybrid approach employs a DE algorithm for identifying promising regions of the solution space followed by use of Gauss-Newton method to determine the optimum in the identified regions. Some well-known benchmark estimation problems are utilized to test the efficiency and the robustness of the proposed algorithm compared to other methods reported in the literature. The comparison results indicate that presented hybrid algorithm outperformed other estimation techniques in terms of the global searching ability and the convergence speed. Additionally, study of kinetic model parameters for an irreversible, first-order reaction system was carried out to test the applicability of the proposed algorithm. The suggested method can be used to estimate suitable values for the model parameters of a complex mathematical model.