Hybrid Optimization Approach Using Multiobjective Genetic Algorithm NSGA‐II, SCAPS‐1D Simulation, and Response Surface Methodology for Organic Solar Cell Analysis

In the field of simulation, it is difficult to find the relevant values for the properties of materials and in this context this approach has been proposed on optimizing the performance of organic solar cells, a promising technology in the field of renewable energy, to increase their efficiency. It adopts a hybrid approach combining the response surface methodology (RSM) with a Box–Behnken design (BBD) and the nondominated sorting genetic algorithm II (NSGA‐II). The RSM BBD method is used to identify objective functions to be optimized, considering interactions between selected parameters such as the thickness of the active layer, electron‐transport layer (ETL), hole‐transport layer (HTL), and the doping of these layers. Concurrently, the NSGA‐II genetic algorithm aims to maximize the performance of the solar cell based on these parameters. The specific importance of NSGA‐II lies in its ability to solve complex multiobjective optimization problems. Indeed, NSGA‐II is designed to simultaneously manage several performance objectives, which is crucial for organic solar cells. Its ability to generate a diverse set of optimal solutions enables efficient configurations to be found that may not be obvious with simpler optimization approaches. The results of this study show that optimum solar cell performance is achieved with active layer, ETL layer, and HTL layer thicknesses of 100.86, 79.9, and 20.24 nm, respectively, and active layer doping of 8.71E + 21 cm −3 , HTL layer doping of 9.90E + 21 cm −3 , and ETL layer doping of 9.49E + 21 cm −3 . Analysis using Solar Cell Capacitance Simulator‐1D (SCAPS‐1D) software shows that optimum performance is achieved with these specific parameter values. After optimization with NSGA‐II, the power conversion efficiency increases by 39% compared to previous work. This study provides evidence of the effectiveness of the proposed hybrid approach for optimizing the performance of organic solar cells. By showing remarkable agreement between the results obtained by NSGA‐II and SCAPS‐1D, this approach opens up promising prospects for the future of renewable energy.