A hybrid differential evolution algorithm for nonlinear parameter estimation of kinetic systems

The determination of the optimal model parameters for kinetic systems development of kinetic models 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. The computational results indicate that the global searching ability and the convergence speed of this hybrid algorithm are significantly improved. 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.