###### Abstract  
In the recent past, the solar photovoltaic (PV) system has emerged as the most promising source of alternative energy. This solar PV system suffers from an unavoidable phenomenon due to the fluctuating environmental conditions. It has nonlinearity in I-V curves, which reduces the output efficiency. Hence, the optimum maximum power point (MPP) extraction of the PV system is difficult to achieve. Therefore, for maximizing the power output of PV systems, a maximum power point tracking (MPPT) mechanism, which is a control algorithm that can constantly track the MPP during operation, is required. However, choosing a suitable MPPT technique might be confusing because each method has its own set of advantages  
However, choosing MPPT for specific PV system designs and situations might be confusing, since each approach has its own set of benefits and drawbacks. Numerous reviews on the subject have been published to aid academics and field engineers to choose the optimal MPPT for a certain PV system. The conventional MPPT techniques are regarded by the majority of studies as a successful way of obtaining maximum output power from solar PV systems under nonuniform irradiation [4; 5; 6; 7; 8; 9]. Partial shadowing or mismatched sun irradiation conditions were also explored in a few works of literature [10; 11; 12; 13]. Furthermore, [PERSON] et al. [14] proposed swarm intelligence-based MPPT approaches as a feasible alternative. Although, only a few papers [9; 15] have focused on MPPT under uniform solar irradiation and PSC. In the literature, the various types of conventional MPPT techniques developed so far are fractional short circuit current (FSCC) [16; 17], fractional open circuit voltage (FOCV) [18], curve fitting (CF) [19], constant voltage (CV) [20], hill climbing (HC) [21], incremental conductance (INC) [22] and ripple correlation control (RCC) [23] methods. The main shortcoming of the FSCC and FOCV methods is that they are less accurate; hence, they are primarily used in low-power applications [24]. While well-known approaches such as P&O, HC, and INC can only measure maximum power for uniform irradiation, these techniques completely fail when partial shade occurs, that is, when panels in an array receive uneven irradiation [25; 26; 27]. Additionally, these approaches exhibit poor convergence, slow tracking speed, and excessive steady-state oscillations. As a result, traditional methods must be combined with alternative methods to track MPP under partial shading conditions (PSC). In the meantime, a novel adaptive P&O technique that uses variable step sizes rather than fixed step sizes is being developed [17; 28]. Despite the fact that the preceding modifications have improved their performance, they are insufficient under all operating conditions [29; 30; 31]. To address the shortcomings of conventional MPPT techniques, swarm intelligence techniques have been introduced in the field of solar PV systems research [32; 33; 34]. These approaches are regarded as the best options for non-linear optimization because of multiple advantages, such as (a) wide exploration in search space; (b) ability to manage nonlinearity and (c) the coherence of their ability to locate global optimal regions. The MPPT approaches that fall under swarm intelligence techniques are artificial neural network (ANN) [35; 36; 37], fuzzy logic control (FLC) [38; 39; 40; 41], be colony search algorithm [42; 43], differential evaluation [44; 45], particle swarm optimization (PSO) [46; 47; 48; 49; 50; 51; 52], cuckoo search [53; 54], genetic algorithm (GA) [55; 56; 57; 58; 59], fireflies algorithms [60], ant colony optimization [61; 62], chaotic approach [63], nonlinear control [64], and random Search methods [65]. Furthermore, under partially shadowed conditions, these algorithms are substantially affected in terms of reaching the global maximum [66]. Because the quantity of required training is fairly extensive, its execution is made even more difficult.