Using Modified Self-adaptive Differential Evolution for Estimation of Chemical Reaction Kinetic Parameters

In order to accurately estimate the reaction kinetic parameters, a novel modified self-adaptive differential evolution (MSaDE) algorithm is proposed in this paper, aiming at the problems of premature or local optimization of differential evolution (DE). MSaDE advances a mutation strategy candidate pool, a given interval of mutation factor (F) and crossover factor (CR). In the evolutionary process, mutation strategy, F and CR of the next generation are self-adaptively adjusted according to the previous state information of search process. Through five commonly used benchmark functions, it shows that MSaDE can effectively improve the global optimal searching capability, with higher search accuracy and faster convergence rate. In addition, MSaDE is applied to estimate the kinetic parameters of ammonium perchlorate (AP) thermal decomposition reaction models, satisfactory results are obtained.