Optimal Parameter Identification of Perovskite Solar Cells Using Modified Bald Eagle Search Optimization Algorithm

Abstract

In this paper, a modified bald eagle search optimization algorithm was applied for the first time to determine the parameters of the triple diode model (TDM) of perovskite solar cells (PSCs). Two experimental datasets are considered; the first is measured I–V points for a PSC at standard conditions. The second consists of the measured I–V points for a modified PSC. In contrast, the cost function to be minimized is the root mean square error (RMSE) between the experimental dataset and the calculated one. To prove the superiority of modified bald eagle search optimization (mBES), a comparison with the original bald eagle search optimization (BES), particle swarm optimizer (PSO), Hunger games search (HGS), and recent Coronavirus Disease Optimization Algorithm (COVIDOA) was implemented. Furthermore, statistical analysis of ANOVA and Tukey tests was performed. The results demonstrate the lead of the recommended mBES in identifying the parameters of the TDM for PSCs, where the RMSE achieved the least value among the used optimization algorithms in this study.