## 1 Introduction  
Greater awareness of clean energy has undoubtedly forced many governments and private sectors to adopt renewable energy resources [1, 2]. Typically, the energy demand in a household is around 7% of the total energy usage daily [3, 4]. Mainly, the energy supply is driven by fossil fuels. For many decades, fossil fuel has been used in modern civilization as the main source of electricity. The burning of fossil fuels causes global warming. The carbon emitted from fossil fuel has become an environmental pollutant. Thus, the need for low carbon-based sources of electricity (green energy) such as wind, solar, biomass, etc., is essential in the coming future [5, 6]. Among these sources, solar photovoltaic (PV) energy is seen as the best potential candidate due to its availability and cleanliness [7, 8, 9, 10]. Thus, due to this interest, focusing on solar PV energy is prioritized. Operating a PV system to the utmost efficiency is essential for more commercial and economic reasons [2]. However, due to the non-linear characteristics of PV cells and rapid changes in environmental conditions such as temperature and irradiance, the effort of tracking maximum PV power may become challenging [11; 12]. The optimal performance of a PV system can only be realized by incorporating the maximum power point tracker (MPPT) algorithm in conjunction with the power converters [13; 14]. Maximum power is extracted by the MPPT algorithm from the PV array. Numerous MPPT techniques proposed by researchers are capable of tracking the optimum power of the PV system. Typically, the most preferable methods are Perturb and Observe (P&O) [15; 16; 17] and Incremental Conductance (IC) [18]. These two conventional methods generally work well under uniform conditions and are easy to implement [19]. The high demand for high maximum power output from the PV source can only be realized by connecting several PV panels in series and parallel to meet the current and voltage of the system. Thus, due to the large footprint of PV panels, the environmental condition such as shadows from buildings, trees, and moving clouds are unavoidable [20]. The situation may be worsened when considering non-uniform irradiances that penetrate each PV module [21]. This creates an unwanted condition known as partial shading condition (PSC) [22]. Under PSC, few peaks occur in the power-voltage (P-V) and current-voltage (I-V) curves. Those unreal peaks are known as the local maxima power point (LMPP), and only one real peak is known as the global maxima power point (GMPP) [23; 24; 25; 26]. The conventional P&O and IC methods would most likely fail to distinguish the GMPP. The algorithms of P&O and IC may get trapped at a local peak which significantly affects the energy conversion and lowers the PV system efficiency [2; 27; 25; 2]. Despite this disadvantage, both techniques also suffer from continuous oscillation around the MPP area. Various efforts are carried out to reduce the oscillation but this is accomplished to the extent of reducing the tracking speed [28; 29]. Soft computing techniques have been adopted by several researchers to mainly overcome the problem associated with shading conditions [30; 31; 32; 33]. A few notable ones such as Fuzzy Logic Control (FL) [29] and Particle Swarm Optimization (PSO) [34; 35] are implemented by researchers in tracking the global maximum power point (GMPP). PSO is the most preferable method among all due to its simple formula, uncomplicated implementation, high efficiency, fast response, and zero steady-state oscillations [34; 36; 37]. However, recent studies indicate that PSO has intermittent performance under PSCs [38]. PSO tends to fail at LMPP under certain shading conditions [39]. In the PSO algorithm, the challenge is to define the random coefficient between the exploration and exploitation parameters.