## 7 Current Trends and Future Directions  
Current trends in MPPT algorithms are increasingly focused on integrating AI and ML to enhance adaptability and performance under variable conditions. Hybrid approaches, which integrate both traditional and advanced techniques, are gaining popularity due to their ability to combine global search capabilities with local tracking accuracy. Real-time adaptive MPPT systems are on the rise, allowing for dynamic adjustments [105]. Looking ahead, future developments are expected to include the application of advanced AI-based techniques, the potential use of quantum computing for complex optimizations, integration with smart grids, and improved environmental and economic optimization, all aimed at achieving more efficient and responsive PV systems. MPPT algorithms may evolve as a result of these advances [103, 106]. Table 9 provides a summary of the current trends and applications for both traditional and advanced MPPT algorithms. Deep learning and reinforcement learning are methods used in the optimization of solar PV systems. Large data sets increase the adaptation of deep learning. Thus, this makes MPPT algorithms for solar PV systems more sensitive. In a study conducted at Bilecik Seyh Edebali University [107], it was shown that artificial intelligence-based MPPT systems using deep learning exhibited more efficient performance than classical MPPT algorithms. Reinforcement learning, on the other hand, provides better adaptation to environmental changes. In a simulation study [108], it was seen that the reinforcement learning-based MPPT controller gave very good results under different operating conditions, and varying temperatures, radiation intensities, and electrical loads in the simulation. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type** & **Tracking Algorithm** & **Advantages** & **Disadvantages** \\ \hline Traditional & P\&O & Simple to implement. & Can oscillate around the MPP. \\  & IC & Better in varying conditions. & & More complex and computational. \\  & CV & Straightforward for stable systems. & & Ineffective with changing conditions. \\  & LT & Easy with known conditions. & & Not adaptive to changes. \\  & HC & Simplicity. & Can oscillate around the MPP. \\ Advanced & FLC & Handles uncertainties well. & & Complex rule setup \\  & ANN & Adapts to complex behaviors. & & Needs extensive training \\  & FS & Efficient for specific problems. & & May not always converge well. \\  & PSO & Effective exploration. & & Computationally expensive. \\  & GWO & Handles complex conditions. & & Requires parameter tuning. \\  & ACO & Good for dynamic systems. & & Slow convergence. \\  & ABC & Exploration and exploitation. & & Sensitive to parameter settings. \\  & CSO & Efficient for complex spaces. & & High computational cost. \\  & FO & Effective for multi-modal issues. & & Expensive and requires tuning. \\  & FP & Mimics natural processes. & & Less effective in discrete problems. \\  & GA & Flexible and robust. & & Slow convergence. \\  & PSO-P\&O & Exploration with simplicity. & & Inherris P\&O’s oscillation issues. \\  & GWO-P\&O & Merges global search with P\&O. & Complex and computationally intense. \\  & & Dynamic step-size & & Increased complexity, needs \\  & & adjustment. & & tuning. \\  & GA-P\&O & Precise tracking. & & High computational cost. \\  & ANFIS & Robust and adaptive. & & Requires significant training. \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison of traditional and advanced MPPT algorithms [53, 103, 104]. Advanced MPPT algorithms often require substantial computational resources due to their complex mathematical operations, iterative processes, and real-time data analysis. This can lead to increased processing time and power consumption, especially in embedded systems with limited processing power or energy constraints. Balancing accuracy and performance is crucial, as these algorithms require more sophisticated hardware for fast data processing and decision making.