###### Abstract
This method has better capability and efficiency in different test conditions, but it depends on the system. The artificial neural network (ANN)-based MPPT algorithm [16] is applied in order to use ANN PSC data for finding the GMPP. In [17], an MPPT method is proposed, and each PV configuration connected to the converter is integrated into the MPP detector. The results show that the tracking efficiency is good but the cost of implementation is high. In [18], a fast GMPPT method is presented based on the PSO for a photovoltaic (PV) string under PSC using a boost DC/DC converter. In [19], the Bat Algorithm (BA) is used to track the MPP of the PV systems in different PSCs. The BA is a suitable algorithm for achieving the GMPP of a PV system. In [20], the MPPT of the PV system via the gray wolf optimizer (GWO) method, inspired by gray wolf hunting behavior, is used to achieve the GMPP. The results indicate the optimal performance of the MPPT method with the GWO in extracting the MPP. In [21], the MPPT problem is developed using the flower pollution algorithm (FPA) to increase the efficiency of the PV system MPPT in partial shading conditions. In [22], type2-fuzzy is presented to perform the MPPT in standard and PSCs. The obtained results demonstrate the desirable performance of the proposed method in the maximum extraction of the PV power. In [23], the MPPT algorithm is developed using atom search optimization (ASO) to improve the GMPP tracking efficiency of the PV system. In [24], the multi-verse optimizer (MVO) and in [25], the manta ray foraging optimization (MRFO) are performed with MPPT solving for the PV system in order to achieve the GMPP in standard and shading conditions. Different models of PV cells are proposed for solving the MPPT problem. The literature review showed that the single-diode model for PV cells is frequently used as a conventional model for MPPT problems. Therefore, the evaluation of the single-diode model as one of the desirable models for MPPT solving in PSCs is considered in this study. In MPPT solving, the maximum power of PV systems should be obtained. Thus, based on the ratio of the power extracted from the photovoltaic system to the global power of the photovoltaic configuration, and based on the use of converters, we increase the efficiency of this type of system. However, some systems may not be easily traceable due to their complexity and control structure. Therefore, tracking such systems is associated with high tracking costs, and the intended goal of improving their efficiency may not be achieved. In studying the tracking of the maximum power point of photovoltaic systems based on some traditional methods such as the observation disturbance method, short circuit current method, open circuit voltage method, and climbing method, in most cases, these are able to achieve the optimal global solution although they are not tracking systems. Efforts have been made to combine these methods with optimization algorithms to enhance their performance. Therefore, using traditional methods, in addition to spending money and wasting it, cannot guarantee a significant improvement in the efficiency of photovoltaic systems. For this reason, this paper uses a new intelligent optimization method to solve the tracking problem. Moreover, the investigation of a literature review demonstrated that the application of meta-heuristic algorithms with high capability in optimization is still required to enhance the PV systems' GMPP tracking in PSCs. This is because one meta-heuristic algorithm may operate desirably in some optimization problems, but the same method in solving another problem may not achieve the optimal global solution and does not work well even in the face of system condition changes. It is also better to use algorithms with fewer control parameters. By increasing the number of control variables of optimization algorithms and their high sensitivity to these parameters, the global optimal solution is not achieved, and the local optimal is also trapped.