Abstract
Abstract In this study, we introduce a novel method that can be seamlessly integrated into existing metacognitive algorithms, significantly enhancing their performance during both exploitation and exploration phases. This method offers several advantages, including ease of implementation and simplicity in calculations, which collectively accelerate convergence to the global minimum and enhance the algorithm's robustness. Notably, it effectively avoids local minima, ensuring the algorithm does not become trapped. Furthermore, this method eliminates the need for developing new metacognitive algorithms. To demonstrate its benefits, we apply this method to the crow search optimization algorithm (CSA), which is notably deficient in convergence speed, robustness, stability, and escaping local minima. Consequently, the enhanced algorithm is termed the diligent crow search optimization algorithm (DCSA). Additionally, we utilize the powerful DCSA algorithm to identify the parameters of solar cells, aiming to maximize power output from solar energy—a critical global concern. To evaluate the proposed algorithm, we tested it on various solar cell models, including one-diode, two-diode, and three-diode configurations, as well as several widely used solar panels such as SM55, KC200GT, and SW255. We also examined the impacts of radiation, temperature, and unknown parameters on these solar panels. The simulation results demonstrate that implementing the proposed method on the crow algorithm resulted in a 98% improvement in stability and a sevenfold increase in convergence speed.