The model forces to pass through these points after that Evolutionary Algorithm (EA) is applied to fine-tune the remaining section of the suitable curve. The EA technique shows substantial enhancement for fitting accuracy. From the MATLAB/Simulink results, it is concluded that the suggested method constantly converges to an optimum solution in less than 50 iterations but increases the system complexity [12]. The authors in [7] also proposed many techniques based on artificial intelligence like neural networks & fuzzy logic control. In the Artificial Neural Network (ANN), the input combination is considered as the surrounding condition of PV parameters, and output is assumed to be either reference voltage or duty cycle. As compared to the conventional neural network (NN) technique for MPPT, a multi-level neuro-fuzzy model has been proposed. The proposed model has been proven better efficient under a wide range of operating circumstances [13]. However, as the characteristic of PV array changes, the system needs to train periodically to obtain high accuracy [14]. The main benefits of Fuzzy Logic Control (FLC) is the acceptance of the noisy input signal and its ability to converge during a sudden change in surrounding conditions [15; 16]. However, this technique determined the error based on a rule base table. The drawback of this method is that it highly depends on the availability of previous knowledge of the input signal [7]. In the literature, the Takagi-Sugeno model is used for further improvement in the stability and robustness of the system [17; 18]. State-Space (SS) based MPPT technique uses the SS model to represent PV systems and dynamic feedback controllers to track MPP. Due to the ease of change in system parameters, this technique is very suitable even in fast-changing weather conditions and partially shaded conditions. However, no experimental verification is given [19; 20]. The Slide Control Method (SCM) is achieving MPP that achieved the control of DC/DC converters. This technique normally employs the hysteric mode control of the DC/DC converter. The leading advantage of this method is its dynamic response and fast speed. Although, the unstable switching frequency is its major drawback [21]. Another MPP Flower Pollination Algorithm (FPA) has been introduced by the researchers in which five pollens or duty cycles are initially generated with an FPA between 0-1 and measure the power of each pollen. The pollen that gives the maximum power is chosen as the best power. To deciding which pollen will pass through global pollination or local pollination, a new number between 0-1 is randomly generated that has a 0.8 switching probability \"P\" for achieving the power. Researchers concluded that the best power gives the global best value after 25 iterations only [22]. The major disadvantages of FPA are high computation time, difficulty to tune parameters, complex structure, and procedural complications [23]. Modified FPA is described for the MPP of the PV system. The performance of the system is improved but the structure of FPA has become more complex [24]. [PERSON] et al. [25], examined the MPP using global optimization issues known as Random Search Method (RSM) as it tracks the MPP using a random solution. In defined limits, [PERSON] et al. produce the solution randomly and search for appropriate solutions that have a range between 0-1. To measure the power, these values send to the DC-DC converter against each input. Furthermore, these values are restructured continuously until the MPP is obtained after each iteration. In [26] a Modified Firefly algorithm (FA) for the calculation of MPP is elaborated. To decrease the randomization of fireflies, randomization \(\alpha\) and \(\beta\) parameters are introduced by varying irradiance and temperature. From the result, the authors of [26] concluded that efficiency and tracking speed is improved as compared to the conventional FA. The bypass diodes are reverse biased and have little effect under UI levels.