## 6 Discussion  
Both traditional and advanced MPPT algorithms play crucial roles in optimizing PV systems, each offering distinct benefits and addressing different needs within the field of renewable energy. Due to their simplicity and ease of use, traditional MPPT algorithms like P&O and IC have long been the basis of solar power optimization. They are particularly effective in stable or predictable environments, where their basic approaches can efficiently track the MPP with minimal computational resources. However, they may struggle with rapidly changing environmental conditions or in systems requiring high precision. In contrast, advanced MPPT algorithms are designed to handle the complexities of modern, dynamic environments. These algorithms offer enhanced performance through their ability to adapt to varying conditions, optimize across multiple objectives, and improve tracking accuracy. While they come with increased computational demands and complexity, their advanced capabilities make them well-suited for large-scale PV systems, smart grids, and other sophisticated applications. As a result, in environments where traditional methods are inadequate, smart, optimization and bio-inspired-based tracking methods have surfaced. Moreover, there is a tendency to apply such combinations of critical and operational advances as PSO-P&O and GWO-P&O, which seem to be effective due to the combination of the global applicability of modern methods and the local effectiveness of traditional methods. MPPT algorithms typically use electrical or atmospheric data to find the MPP. Analog MPPT algorithms rely on op-amps or simple circuits, which are inexpensive, simple, and require no programming, controllers, or memory. However, they lack flexibility. Digital-based algorithms, which include a controller or processor, can be modified through changes in code or flowcharts, but this increases complexity. There is a trade-off between MPPT algorithm complexity and tracking efficiency. Traditional methods like P&O and IC are simple with low computational needs but may be less efficient under rapidly changing or shaded conditions. Advanced techniques provide better tracking in non-ideal conditions but require more computational power and hardware. The choice depends on the PV system's application and constraints. For systems with limited resources or cost concerns, simpler methods are preferable. However, for critical applications where efficiency is key and resources are available, advanced techniques offer significant benefits despite their complexity and cost. Partial shading can significantly impact the performance of PV systems, as it creates multiple LMPPs on the power-voltage curve, confusing traditional MPPT algorithms like P&O and IC. These traditional methods often fail to identify the true global MPP under partial shading, leading to inefficient energy harvest. Advanced MPPT algorithms, such as metaheuristic techniques, are better equipped to distinguish between local MPPs and global MPP, ensuring more accurate tracking and maximizing energy output. These algorithms offer superior performance, especially in complex shading conditions. Table 7 classifies the performance of MPPT algorithms with respect to the computational requirements and overall efficiency under uniform and PSC into low, lower-medium, medium, upper-medium, and high levels. \begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Tracking** & **Sensed** & **Circuity** & **Tracking** & **Implementation** & **True** & **Accuracy** & **Cost** \\
**Algorithm** & **Parameters** & **Speed** & **Complexity** & **MPPT** & **Accuracy** & **Cost** \\ \hline PSO-P\&O & V, I & D & Fast & Complex & Yes & High & Medium \\ GWO-P\&O & V & D & Medium & Complex & Yes & High & Medium \\ FLC-P\&O & V, I & D & Fast & Medium & Yes & Medium & Medium \\ GA-P\&O & V, I & D & Fast & Complex & Yes & High & High \\ ANFIS & V, I & D & Fast & Complex & Yes & Medium & High \\ \hline \end{tabular}
\end{table} 
Table 6: Comparison of hybrid MPPT algorithms [36].