Metaheuristic optimization of perovskite solar cell performance using Taguchi grey relational analysis with grey wolf optimizer - Salehuddin, Fauziyah; Mat Junos @ Yunus, Siti Aisah; Ahmad Jalaludin, Nabilah; Mohd Nazli, Nurul Farina; Arith, Faiz; Mohd Zain, Anis Suhaila; Mohamd Rafidi, Nur Ruzanna; Kaharudin, Khairil Ezwan (Taylor's University, 2025)
Perovskite solar cells offer numerous benefits like simplified production, adaptability, and affordability in contrast to silicon-based counterparts. Yet, enhancing their power conversion efficiency remains difficult due to the diverse effects of various layer parameters variability. This research work proposes the utilization of metaheuristic approach in optimizing multiple layer parameters of Perovskite Solar Cell (PSC) for better output properties. The metaheuristic approach sequentially employs the L27 orthogonal array (OA) Taguchi-based design of experiment (DoE), Grey Relational Analysis (GRA), Multiple Linear Regression (MLR) and Grey Wolf Optimizer (GWO). The L27 OA Taguchi-based DoE is initially employed to mine sufficient output data simulated using one dimensional solar cell capacitance simulator (SCAPS-1D). GRA is utilized to merge the Jsc and PCE into a single representative grade called Grey Relational Grade (GRG) for holistically improved PSC performances. MLR is then performed to establish the linear relationship between layer parameters and the computed GRGs, thereby modeling the objective function. The best solutions of the MLR model are finally predicted by using GWO algorithm where both Jsc and PCE are successfully optimized to 25.67 mA/cm2 and 24.73%, respectively.