PROJECT TITLE :
On the Performance of Multiobjective Evolutionary Algorithms in Automatic Parameter Extraction of Power Diodes
During this paper, a general, strong, and automatic parameter extraction of nonlinear compact models is presented. The parameter extraction is based on multiobjective optimization using evolutionary algorithms, which permit fitting of many highly nonlinear and highly conflicting characteristics simultaneously. 2 multiobjective evolutionary algorithms that have been proved to be robust for a wide selection of multiobjective issues [one]-, the nondominated sorting genetic algorithm II and therefore the multiobjective covariance matrix adaptation evolution strategy, are utilized in the parameter extraction of a unique power diode compact model primarily based on the lumped charge technique. The performance of the algorithms is assessed employing 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 therefore the potency and effectiveness of the proposed multiobjective optimization scheme.
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