## 1 Introduction  
Solar energy has become increasingly important as an energy resource due to its inexhaustible nature, cleanliness, scalability in power generation and low maintenance requirements. As a country with high sunshine, India offers an exceptional opportunity for harnessing solar energy, which can significantly address our pollution problems. In recent times, photovoltaic systems have gained immense popularity due to their numerous advantages. These systems are known for their sustainability, low maintenance requirements, absence of complex parts and extended lifespan, among other benefits [1]. The PV systems are subjected to shading effects along with dynamically changing weather conditions due to the shadow of large buildings, passing clouds, birds' shadows, etc. In the case of Partial Shading Condition (PSC), the PV modules get different irradiations and temperatures, leading to decreasing the power output obtained from the PV array and causing hot spots and loss of tracking efficiency. During shading, power variation with voltage characteristic plot possesses several peaks, one referred to as Global Maximum Power Point (GMPP), and others are well known as local maximum power points, as delineated in Figure 1.  
Most conventional MPPT approaches like Perturb and Observe (P&O), Incremental Conductance (INC) and Constant Voltage (CV) techniques can work efficiently when solar radiation and temperature are uniform but significantly fail when the weather conditions are varying. In order to address the mentioned challenge, researchers have utilized intelligent global search optimization algorithms which effectively tackle the global maximum power tracking (MPPT) problems. In the available literature, diverse MPPT techniques and alternative solutions have been employed to accurately identify the true global maximum power point amidst various local MPPs. These popular intelligent algorithms include (a) Genetic Algorithm (GA), (b) Particle Swarm Optimization (PSO), (c) Ant Colony Optimization (ACO) and (d) Teaching learning-based optimization. [PERSON] et al. [2] implemented the GA with an ANN-based tracking method for the standalone PV system. Genetic algorithm optimization was used in this algorithm to optimise the required number of neutrons in a multi-layer perception neural network, thereby tracking required power during uneven irradiance situations. [PERSON] et al. [3] suggested a superior Differential Evolution (DE) algorithm for changing solar environmental conditions to obtain global power output. [PERSON] et al. [4] introduced an adaptive differential algorithm that can track MPP under swiftly changing climatic conditions. [PERSON] et al. [5] surveyed methods resembling artificial intelligence and hybrid algorithm for tracking MPP and summarized the finer points of various techniques understanding to increase the power output of the PV array. [PERSON] et al. [6] applied the PSO for various operating situations and reported the merits of the PSO algorithm, which includes ease of implementation, system independence and enhanced output. [PERSON] et al. [7] discussed the PSO algorithm, which eliminates the need for the direct duty ratio method's proportional-integral (PI) control block. [PERSON] et al. [8] used the PSO algorithm to control the gate pulse given to the boost converter to extract GMPP. [PERSON] et al. [9] used the PSO algorithm by taking additional specific coefficients of the algorithm to progress to the next step and get the global point of the PV. The usage of more specific parameters in PSO algorithms results in decreased tracking efficiency and increased uncertainty of the solution. Therefore, the desired operating point cannot be reached. To overcome these drawbacks, improved optimization algorithms were proposed by the researchers. [PERSON] et al. [10] offered a modified PSO algorithm to harness GMP from a PV system during a cloudy day. The adjustment practice fallouts to figure out the algorithm-specific parameters for the next step of acceleration. In [11], [PERSON] et al.