On the Performance of Multiobjective Evolutionary Algorithms in Automatic Parameter Extraction of Power Diodes

In this paper, a general, robust, and automatic parameter extraction of nonlinear compact models is presented. The parameter extraction is based on multiobjective optimization using evolutionary algorithms, which allow fitting of several highly nonlinear and highly conflicting characteristics simultaneously. Two multiobjective evolutionary algorithms which have been proved to be robust for a wide range of multiobjective problems [1]-[3], the nondominated sorting genetic algorithm II and the multiobjective covariance matrix adaptation evolution strategy, are used in the parameter extraction of a novel power diode compact model based on the lumped charge technique. The performance of the algorithms is assessed using a systematic statistical approach. Good agreement between the simulated and measured characteristics of the power diode shows the accuracy of the used compact model and the efficiency and effectiveness of the proposed multiobjective optimization scheme.