efficient solar cell design through grey wolf optimization: multi-layer thickness optimization for enhanced short-circuit current density

Optimization problems can be classified as discrete or continuous, depending on the nature of the decision variables. using continuous algorithms for problems that are inher-ently discrete can pose challenges in achieving optimal efficiency. this study aims to reduce the number of simulations required to determine the most suitable layer thick-nesses for a solar cell. the focus is on the grey wolf optimization method, a continuous algorithm known for its effectiveness in handling complex problems. the objective function of the algorithm is to maximize the short-circuit current density. the research is conducted in two primary stages: single-layer optimization and multi-layer optimization. in the single-layer optimization phase, the zno optical spacer layer and the moox layer are optimized individually using the grey wolf method. the results indicate that the grey wolf algorithm requires significantly fewer simulations compared to both the genetic algorithm and the brute-force method, which are discrete optimization strategies. in the multi-layer optimization phase, the simultaneous optimization of two layers using the grey wolf method requires approximately 307.03 ± 169.46 simulations, with 100% accuracy. although there is a minor difference in outcomes, as evidenced by the highest standard deviation, this continuous optimization method still requires considerably fewer simulations compared to the brute-force method (2511 simulations) and the genetic algorithm (1758.77 ± 39.75 simulations) reported in previous studies.