## 1 Introduction
[6] introduces a hybrid MPPT strategy that integrates Particle Swarm Optimization (PSO) with conventional methods such as P&O and incremental conductance (IC). The hybrid approach dynamically adjusts the perturbation step size in response to changing solar irradiance, eliminating the limitations of fixed step sizes in traditional methods. In [7], an enhanced adaptive step-size P&O algorithm is presented, optimized for operation under various levels of partial shading. The results reveal the significant limitations of conventional methods like P&O and IC under partial shading, underscoring the need for advanced control strategies. These studies collectively demonstrate a shift toward intelligent MPPT algorithms that improve accuracy, response time, and reliability, especially under dynamic environmental conditions. Consequently, optimizing global peak power tracking in PV power systems under uniform and partial shading conditions is crucial for improving overall system performance in terms of power output, reliability, efficiency, and power quality [8; 9]. Partial shading conditions can greatly affect the performance of PV energy systems, prompting the development of various mitigation strategies. These strategies are broadly classified into soft-computing (artificial intelligence and bio-inspired) and hybrid MPPT approaches for mitigating the shading effects on PV applications, both of which play essential roles in minimizing the impact of shading on PV system efficiency [10]. Ref. [11] proposed a method capable of tracking not only the global peak but the sum of all available peaks using an interleaved boost converter (IBC). This approach significantly outperforms conventional boost converter techniques, enhancing system performance in terms of power quality, mismatch losses, and overall energy extraction under certain partial shading scenarios. Furthermore, the stability of DC-bus voltage plays a crucial role in the reliability and efficiency of standalone PV systems. Voltage ripple and instability, especially under rapid load or irradiance changes, can degrade the effectiveness of MPPT techniques. To address these issues, ref. [12] introduced a novel hybrid optimization method combining the salp swarm algorithm and PSO (SSA-PSO) with a direct sliding mode controller. This intelligent control strategy maintains stable output and robust MPPT performance even in the absence of a battery storage system. In [13], a detailed classification of PV-MPPT approaches categorized them into classical MPPT, soft-computing MPPT, hybrid MPPT approaches, and hardware solutions. Classical MPPT techniques like perturb and observe, constant voltage, and incremental conductance rely on simpler algorithms and are effective under uniform conditions but struggle under partial shading. Soft-computing algorithms leverage artificial intelligence and bio-inspired algorithms, including artificial neural networks, fuzzy logic control, genetic algorithm, and particle swarm optimization, providing higher accuracy and efficiency in tracking the GPP under complex conditions like partial shading. In [14; 15], soft-computing techniques, including PSO and modified current sensorless methods, proved effective in managing partial shading circumstances. These approaches provide robustness, flexibility, and reliability, making them well suited for overcoming the challenges associated with partial shading in PV energy systems. On the other hand, hybrid techniques integrate conventional and soft-computing approaches to balance simplicity with accuracy, such as combining ANN with P&O or FLC to overcome individual limitations and enhance performance [16]. Conversely, hardware solutions such as module-based and array-based approaches require physical modifications to PV systems to minimize the adverse effects of partial shading. Several studies have reviewed various mitigation strategies, encompassing both hardware and computational approaches.