Development of hybrid algorithms for EIS data fitting

A general approach is proposed for the development of hybrid algorithms which are designed for efficient fitting of EIS data of different origin. The approach aims to construct two-stage hybrid algorithms, in which different minimization strategies are used, reducing both the computational time and the probability to overlook the global optimum. The best candidates to be implemented at each of the stages of the hybrid algorithms are identified by screening for appropriate combinations of optimization strategies. As an application of this approach, in this work, a hybrid iterative algorithm for the analysis of multidimensional EIS data sets has been developed. The developed algorithm is optimized to fit (in a semi-automatic mode) large experimental datasets to equivalent electric circuits commonly used in physical electrochemistry to model interfaces between solid electrodes and liquid electrolytes.