## 6 Discussion
In the context of hotspot phenomena, traditional and advanced MPPT algorithms behave differently. Traditional algorithms are less effective at detecting and mitigating hotspots, often failing to identify localized temperature variations that can cause reduced efficiency or damage to the PV module. In contrast, advanced MPPT algorithms adapt better to environmental changes, optimizing power output more effectively and preventing overheating in specific regions of the PV module, thus reducing the risk of hotspots. Many MPPT algorithms studied are successfully used in commercial products, such as MPPT-controlled battery charge controllers, string inverters, and solar tracking systems. However, these algorithms are primarily found in academic literature, with most studies conducted in simulation environments. Complex MPPT algorithms are rarely integrated into real-life applications through rapid prototyping and application. Artificial Intelligence (AI) and Machine Learning (ML)-based MPPT algorithms require data sets to function effectively, gaining predictive capabilities through training. However, not all data sets may match the parameters of every solar energy system. For safe operation, the training data must cover the specific parameters of the system in which the algorithms will be applied, ensuring that the minimum, maximum, and optimum values of electrical and environmental data align with the training data and test environment. Additionally, since ML and AI-based algorithms demand intensive processing, they require a controller (regulator or processor) and memory components. The emergence of hybrid algorithms is due to the inability of existing algorithms to provide the desired performance. An algorithm efficient in one area may be less effective in another. For example, traditional P&O and IC MPPT algorithms quickly reach the global MPP point but suffer from oscillations due to their fixed-step nature. Modified algorithms reduce oscillations but take longer to reach the global MPP. Hybrid algorithms combine the strengths of multiple MPPT algorithms, achieving both fast tracking and minimal oscillation by ensuring their harmonious operation. Table 8 presents a comparison of traditional and advanced MPPT algorithms, highlighting their advantages and disadvantages. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Type** & **Tracking Algorithm** & \begin{tabular}{c} **Computational** \\ **Requirements** \\ \end{tabular} & \
\begin{tabular}{c} **Efficiency** \\ **in Uniform Shading** \\ \end{tabular} &
\begin{tabular}{c} **Efficiency** \\ **in Partial Shading** \\ \end{tabular} \\ \hline Traditional & P\&O & Low & Medium & Low \\  & IC & Lower-Medium & Medium & Low \\  & CV & Low & Lower-Medium & Low \\  & LT & Lower-Medium & Medium & Medium \\  & HC & Low & Medium & Low \\ \hline Advanced & FLC & High & High & Medium \\  & ANN & Medium & High & High \\  & FS & High & High & Medium \\  & PSO & Medium & High & High \\  & GWO & Medium & High & High \\  & ACO & High & High & High \\  & ABC & Medium & Upper-Medium & High \\  & CSO & Medium & High & High \\  & FO & Medium & High & High \\  & FP & Medium & High & High \\  & GA & High & High & High \\  & PSO-P\&O & High & Medium \\  & GWO-P\&O & High & Medium \\  & FLC-P\&O & High & Upper-Medium & Medium \\  & GA-P\&O & High & Upper-Medium & Medium \\  & ANFIS & High & Upper-Medium & High \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Performance of MPPT algorithms [25; 27; 29; 34; 37; 38].