### PV Cell Electrical Modelling via Optimization Algorithms  
On the other hand, PV cell electrical modeling is essential for maximizing their performance and successfully incorporating them into power systems. Such models give researchers and engineers a comprehensive picture of the electrical properties and behavior of PV cells, allowing them to forecast a solar panel's performance in different scenarios. Precise electrical models play a crucial role in the design and assessment of photovoltaic systems because they facilitate the knowledge and mitigation of several issues, including aging, shading, and temperature fluctuations, that impact energy output [18]. Electrical models, in particular, make it easier to simulate PV cell I-V (current-voltage) characteristics, which are essential for determining the cells' efficiency and dependability. The characteristics that collectively characterize the performance of the cell are commonly included in these models, and they include things like fill factor, open-circuit voltage, short-circuit current, and series and parallel resistances [19]. A few instances of the several electrical models that have been examined in the literature include the single-diode (SD) model [20] and the double-diode (DD) model [21]. The literature contains a wide range of approaches of different categories in handling these equivalent circuit representations. Analytical techniques use mathematical formulas to determine the unknown parameters [22]. It is possible to broadly classify the many approaches used to derive the assessment parameters for PV models into two categories: analytical methods and Natural-inspired metaheuristics. Analytical techniques include the Lambert W function [23] and the Newton-Raphson [24].  
Numerous scholars are delving into the challenging task of pinpointing the unknown parameters within the PV model due to its multifaceted nature characterized by multiple parameters, nonlinearity, and nonconvexity. In recent years, natural-inspired metaheuristics (NiMH) optimization approaches have been widely used to handle PV system parameter estimate problems, and they have been shown to perform better than conventional methods. These methods imply Particle Swarm Optimizer (PSO) [25], teaching-learning-based optimizer [26], genetic algorithm [27], seagull optimization algorithm [28], differential evolution [29], biogeographic-based optimizer [30], supply-demand optimization [31], bonobo optimizer [32], sunflower algorithm [33], Marine Predator Algorithm (MPA) [34], butterfly optimization algorithm [35], and social networking search technique [36]. The NiMH approaches can effectively handle optimization and analysis challenges because they function as a black box with no restrictions on the issue formulation. This provides NiMH with several distinct features over other techniques. Thus, to address the parametric problems with solar PV cell models, researchers have recently used a range of NiMH [37]. However, most optimization methods identified in the scientific literature have some drawbacks. For instance, an enhanced version of the generalized normal distribution algorithm (GNDA) was presented in [38] in order to model PV system behaviors. In order to enhance the GNDA's performance, two other processes--a premature convergence process and a ranking-based update--were added in this work; however, this mechanism resulted in a computing load that was twice as great as that of the GNDA itself. Additionally, the memetic-adapted differential evolution algorithm did not perform well when interacting with the DD-mode formulation in [39].