## 1 Introduction
focused on applying an enhanced PSO algorithm for MPPT to enhance tracking efficiency, particularly under shading conditions. [PERSON] et al. [12] proposed the adaptive PSO to defeat the de-merits of a conventional PSO. [PERSON] et al. [13] implemented an Improved Pattern Search Method (IPSM) for MPPT, and the solution obtained by the pattern search technique succeeded in their convergence properties. [PERSON] et al. [14] worked on the voltage-band-based improved PSO approach to improve the convergence of the  
Figure 1: P-V plot of PV module under changing shading conditions. PSO. [PERSON] et al. [15] examined the studies on MPPT on cloudy days by applying Enhanced-Leader-Particle Swarm Optimization (EL-PSO), which was more efficient for implementation. [PERSON] et al. [16] presented the benefits of the modified PSO algorithm, highlighting its nearly zero steady-state oscillations and faster dynamic response compared to traditional approaches. [PERSON] et al. [17] commented on the various parameters of the PSO and elaborated on the numerous procedures for MPP tracking. [PERSON] et al. [18] proposed an enhanced PSO method for getting maximum benefits from solar-powered energy integrated through the Z-source inverter and observed various benefits of reduction in the steady state oscillations. [PERSON] and [PERSON] [19] gave an inclusive report on different computing methods (PSO and ACO) to get GMPP beneath diverse shading effects. [PERSON] et al. [20] applied PSO, ACO, Cuckoo Search (CS) and DE for the simulation of the MPPT system during shading patterns, the provision of operation and merits, and the limitations of each optimization algorithm have been highlighted. [PERSON] et al. [21] considered a new GMPP tracking approach which depends on changes in power, besides analysing the nature of the activity. [PERSON] et al. [22] reviewed the various power extraction algorithms and concluded that PSO and CS algorithms have shown more convergence to the global maximum power tracking. [PERSON] et al. [23] investigated the effectiveness of the Particle Swarm Optimization-Support Vector Regression (PSO-SVR) method in reducing the percentage of ripple content and minimizing oscillations in the power waveform around the MPP region. [PERSON] et al. [24] studied the cat swarm optimization in a specific pattern well known as phyllotaxy in order to extract more solar power. [PERSON] and [PERSON] [25] presented a novel Teaching Learning Based Optimization (TLBO) algorithm, that was introduced for the optimal placement of distributed generation in a radial distribution network. This technique offers a unique approach to addressing the problem of determining the best locations for distributed generation sources within the network. [PERSON] and [PERSON] [26] carried out numerous simulations under different shading patterns using (FLC, PSO, and TLBO) and concluded that TLBO extracts GMPP more dynamically than others. [PERSON] and [PERSON] [27] implemented a novel Mine Blast Algorithm (MBA) and applied different patterns of shadow. The obtained results were compared, thereby declaring MBA as superior to TLBO. [PERSON] et al. [28] observed that the TLBO algorithm showed better performance for maximum power point tracking among the optimisation algorithms. [PERSON] et al. [29] found that Firefly Algorithm (FA) was one of the efficient approaches to harnessing maximum solar energy beneath partial shading conditions. [PERSON] et al. [30] studied enhancing the FA algorithm; the study aimed to improve MPPT efficiency and mitigate the adverse effects of partial shading. Comparative analysis between the modified FA and PSO algorithms provided valuable insights into the effectiveness of the modified FA algorithm for achieving superior MPPT performance under partially shaded conditions. [PERSON] et al. [31] proposed a hybrid maximum power point tracking technique using the P&O method with GWO for better MPPT from PV module strings beneath shading conditions. [PERSON] et al.