## 6 Conclusions  
This research work presents a comprehensive study that analyses the challenges faced by three heuristic-optimization-based algorithms in the context of GMPPT. The focus is  
Figure 14: Comparison of output power with different MPPT Algorithms under other shading patterns.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Other Different Shading** & **Parameter** & **ChOA** & **GWO** & **CSO** \\ \hline \multirow{6}{*}{\(G_{3}=\) [750, 850, 600, 800]} & Maximum power @GMPP(W) & 340.06 & 340.06 & 340.06 \\ \cline{2-5}  & Output voltage(V) & 53.67 & 53.21 & 82.46 \\ \cline{2-5}  & Output current (A) & 6.23 & 6.21 & 3.95 \\ \cline{2-5}  & Output power (W) & 334.36 & 329.90 & 325.71 \\ \cline{2-5}  & Conversion efficiency (\%) & 98.32 & 97.01 & 95.78 \\ \hline \multirow{6}{*}{\(G_{4}=\) [600, 800, 400, 200]} & Maximum power @GMPP(W) & 258.29 & 258.29 & 258.29 \\ \cline{2-5}  & Output voltage(V) & 56.41 & 55.41 & 54.32 \\ \cline{2-5}  & Output current (A) & 4.32 & 4.21 & 4.123 \\ \cline{2-5}  & Output power (W) & 243.69 & 233.27 & 223.96 \\ \cline{2-5}  & Conversion efficiency (\%) & 94.34 & 90.31 & 86.70 \\ \hline \multirow{6}{*}{\(G_{5}=\) [600, 200, 800, 250]} & Maximum power @GMPP(W) & 191.22 & 191.22 & 191.21 \\ \cline{2-5}  & Output voltage(V) & 66.31 & 65.31 & 66.21 \\ \cline{2-5}  & Output current (A) & 2.873 & 2.853 & 2.67 \\ \cline{2-5}  & Output power (W) & 188.51 & 186.13 & 176.78 \\ \cline{2-5}  & Conversion efficiency (\%) & 98.58 & 97.33 & 92.45 \\ \hline \multirow{6}{*}{\(G_{6}=\) [400, 600, 800, 100]} & Maximum power @GMPP(W) & 232.52 & 232.52 & 232.52 \\ \cline{2-5}  & Output voltage(V) & 87.54 & 86.46 & 85.44 \\ \cline{2-5}  & Output current (A) & 2.621 & 2.61 & 2.62 \\ \cline{2-5}  & Output power (W) & 229.44 & 225.66 & 223.86 \\ \cline{2-5}  & Conversion efficiency (\%) & 98.67 & 97.04 & 96.27 \\ \hline \hline \end{tabular}
\end{table}
Table 5: _Cont._specifically on the search capabilities of these techniques, both in terms of exploitation and exploration. This article introduces a novel method called ChOA GMPPT to address these challenges. This method improves GMPPT performance by utilizing different deterministic starting points for exploitation and exploration. Simulation tests demonstrate that the proposed ChOA-based GMPPT achieves higher tracking accuracy and conversion efficiency compared to other tested methods. Additionally, it reduces computational complexity and is easy to implement. By providing a discussion and analysis of various heuristic optimization methods in the literature, along with the newly suggested ChoA-based GMPPT approach, this current work enables researchers to select the most suitable method based on their preferences and priorities.  
In future research, the PSO, CSO, TLBO, GWO and ChOA variants are used to optimize the PV system performance under changing weather conditions. There is a possibility for the usage of recent algorithms as well as hybrid algorithms since a comparative study can be done.  
Conceptualization, T.N. and R.D.; methodology, T.N.; software, T.N. and R.D.; validation, T.N.; investigation resources, T.N. and R.D.; resources, T.N. and L.K.; writing--original draft preparation, T.N. and R.D.; writing--review and editing, L.K. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Poznan University of Technology, grant number [0212/SBAD/0594].  
Not applicable.  
The authors declare no conflict of interest.