## 1 Introduction
The second problem is the onset of hot spot. This problem occurs when the _Isc_ current of the shaded module is less than the _Isc_ of the uniformly illuminated modules, so that the shaded module behaves like an energy receiver extracting energy from the other PV modules. This effect can be noticed in the PV curve of a shaded PV panel, depicted in Figure 1. In partially shaded conditions, when the PV system does not receive uniform irradiation, the P-V characteristics become more complex, with multiple local peak displayed and a single global peak. These points are referred to as Maximum Power Points (MPP). Figure 1: Typical curve of a shaded PV panel. In addition to the deformation of the I-V curve, shading may lead to the heating of this module by dissipation of this energy. If the operating point of the shaded module reaches the breakdown voltage, the module will be destroyed by the avalanche effect [13; 14]. Thus, it is crucial to find an optimal operating voltage to extract the maximum amount of PV power when designing the PV system while avoiding the aforementioned problems. A Maximum Power Point Tracking (MPPT) management tool is a required control loop that helps to get the most power out of a PV system. The basic performance of this algorithm is to adjust the duty cycle of the power converter connecting the PV modules to the DC-link. Concerning this tracking technique, various methods were carried out and help improve the energetic performances of the system. The traditional process relies on the incremental method's principle (IC) [15; 16]. It is based on the principle of the zero derivative of the output power \(P\) of the PV panel, with respect to its output voltage V at the point maximum MPP power. In the maximum point, it is positive on the left and negative on the right, so the algorithm tries to find the voltage where this condition holds [17]. The Perturb and Observe (P&O) approach was the focus of further studies [18; 19]. Because of its simplicity and convenience of use, it is frequently implemented. The primary benefit of this method is its direct torque design and lesser rate of monitored parameters. However, it has a significant flaw in terms of chattering on the supplied power form. Classic MPPT methods such as P&O and IC are based on moving the next operating point (OP) in the direction of increasing PV power. However, when partial shading is present, the P-V curve is no longer monastically increasing, as shown in Figure 1. Thus, these conventional methods can only achieve a local MPP and may not reach the global maximum [20; 21]. Therefore, it is necessary to develop an appropriate MPPT algorithm that can get to the global maximum power regardless of the state of illumination on the modules. To overcome this limitation, other strategies based on intelligent optimization, such as the fuzzy logic technique or the neural network method, have been designed in the same sector [22; 23]. The two techniques lead to higher profitability, but their main issue is the database required to adapt these algorithms to PV systems. Optimization algorithms were also used to help resolve this issue of partial shading of the photovoltaic system [24; 25]. In particular, the evolutionary algorithms aim to have an adaptable MPPT tracking method, based on the animal behavior to find food [26; 27]. There is a variety of swarm algorithms, which have been applied in multiple systems such as in [28; 29; 30; 31]. Among them, Particle Swarm Optimization (PSO) [25] and Grey Wolf Optimization (GWO) [32] have shown their reliability to solve real optimization problems where the objective function is not linear. In particular, the works in [17; 33] only considered these two algorithms to configure a DC/DC power converter. The review presented in [34] show that PSO algorithms are still investigated to tune the power converters of microgrids. Moreover, the study elaborated by [PERSON] in [32] presents a comparison between multiple swarm algorithms.