## 1 Introduction  
### Literature Review  
Solar photovoltaic (PV) systems are eco-friendly and help to minimize the greenhouse gas (GHG) emission gases that would otherwise have emerged from the use of fossil fuels for generating electricity. Since solar energy is available in abundance, feeding electricity into the grid through solar PV plants can help in transmitting the much larger amounts of power that might be made from the GHG-intensive network. The increased cost of the PV modules restricts the optimum and maximum utilization of the available renewable energy. Thus, the system's reliability and accuracy in simulated environments must be predicted before the system is actually installed. Modeling solar cells is essential to predict the behavior of the actual solar cell under different environmental conditions and further acquire its characteristic curves; the current-voltage and power-voltage curves. The viable method is to use the analogous electrical circuit, which is based entirely on a source of light-generated current connected to a p-n junction diode. Many frameworks have been developed at different solar intensities and temperature conditions for the simulation of a single solar cell or a complete PV system [1; 2; 3; 4]. Thus, effective and precise solar cell modeling is very crucial to predict its behavior under different atmospheric conditions. Effective modeling of the non-linear characteristics of the PV systems is very beneficial from the point of accurate simulation [5]. In literature, there are various types of stochastic approaches described in the past few decades, such as single-diode, two-diode, and three-diode models, designs with partial shading issues, and many more. The mathematical model of I-V characteristics of PV cells contains a large number of unknown factors. The single and double-diode models are the leading theoretical frameworks for the precise modeling of the system [6]. The single-diode structure is represented by its simplicity and highly acceptable accuracy [7; 8]. The I-V performance feature of the single-diode model has five unknown parameters, i.e., PV current, diode saturation current, ideality factor, parallel and series resistances [9]. This model, however, lacks precision at low irradiance open-circuit voltage as the carrier recombination losses in the depletion region seem to be ignored [10; 11]. To overcome this problem, the double-diode model was introduced. In this model, to represent the recombination losses, an extra diode is used. This model shows better accuracy than the single-diode model. However, it is more complicated than a single-diode model because seven parameters need to be evaluated in the two-diode model in contrast to five parameters in a single-diode model [11; 12; 13]. The three-diode model exhibits better accuracy than the two-diode model, which affects the leakage current and grain boundaries [14]. The three-diode model involves the estimation of nine parameters which is somewhat more complicated than the two-diode model [15]. Hybridizing stochastic optimization algorithms is one of the best ways to develop superior algorithms and exploit numerous algorithm advantages for solving optimization problems [16; 17; 18; 19; 20]. In 2010, the PSOGSA algorithm was proposed, which is a novel hybrid of the PSO technique and GSA algorithm [21]. This algorithm shows a better outcome in solving optimization problems. In this algorithm, agents perform the search process, mimicking GSA behavior in the phase of exploration, and PSO in the exploitation phase. This algorithm was initially introduced to mitigate the GSA's slow exploitation and also mitigate the PSO algorithm's nature to get struck in local minima, which are their key drawbacks. Many studies have been undertaken to improve the performance of the GSA algorithm as well as the PSO algorithm. For solving the clustering problem, the hybrid PSOGSA proved to be the better method for solving the heuristic problems [22].