## 4 Discussion
### Parameter and Optimization Strategy of the Proposed Method for Real-Time Applications  
GAs rely on carefully tuned parameters to perform effectively in optimization tasks. Understanding the criteria for selecting these parameters is crucial, particularly in scenarios where real-time applications require efficient and reliable solutions. Therefore, it is important to first outline the criteria for evaluating the suitability of the optimal values generated by the proposed GA method:  
_Energy Efficiency of the Solution:_ The energy production efficiency of the new configuration generated by the GA should be higher than the previous configuration. In our study, Figure 8 illustrates the increased efficiency achieved after reconfiguration under various shading scenarios. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Evaluation Metrics** & **Dual-Adaptive Method (Proposed)** & **Single-Adaptive Method** & **Fixed Configuration** \\ \hline Energy Efficiency & Very High (Optimized dual-adaptive structure) & High (Single-adaptive structure) & Low (Static connections) \\ \hline Implementation Complexity & Medium-High (Dual-adaptive control) & Medium (Single-adaptive control) & Low \\ \hline Number of Components & High (Additional adaptive panels and dual switches) & Medium (Single-adaptive panel) & Low \\ \hline Dynamic Reconfiguration & Advanced (More precise optimization with dual-adaptive regions) & Present (Partially effective) & Absent \\ \hline Shading Impact Management & Excellent (Dynamic configuration) & Good & Poor \\ \hline Scalability & Good (Due to solution space size) & Very Good (Smaller solution space) & Limited (Static design) \\ \hline Initial Cost & High (Adaptive system and control mechanisms) & Medium & Low \\ \hline Long-Term Performance & High (Optimized energy gain) & Medium-High & Medium-Low \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Performance, design features, and trade-offs among dual-adaptive, single-adaptive, and static PV configurations. _Convergence Time_: Since the reconfiguration process must occur quickly, the solution produced by the GA is expected to yield optimal results within an acceptable timeframe. Table 4 presents the simulation results for Scenario 1 under specific irradiation conditions, showcasing the performance of the GA under different parameters, particularly in terms of convergence time. _Stability of the Solution_: The reconfigured configuration produced by the GA should exhibit stable performance under varying shading conditions. The fitness value optimized by the GA is the CVI. Minimizing this value directly corresponds to maximizing efficiency (Section 2.6). The key point here is that the CVI value must be successfully optimized under diverse shading conditions. Our study relies on the pre-optimization of parameter ranges for the genetic algorithm, as continuous optimization in real-time applications may not always be feasible. The results presented in Table 4 demonstrate that the optimized parameters ensure acceptable convergence times and energy efficiency. For instance, setting the population size to 100 allowed the system to quickly reach the global optimum while maintaining comprehensive coverage of the solution space. Selecting a crossover rate in the range of 0.6-0.8 preserved genetic diversity, and setting the mutation rate between 0.04 and 0.16 improved solution stability and ensured population diversity across various shading scenarios. Additionally, the results presented in Tables 7 and 8 confirm the stability of the optimized parameters in terms of CVI and convergence times. The low standard deviation values for CVI in Table 7 demonstrate the consistency of the algorithm's results even under different shading conditions. The results in Table 8 further show that the optimized parameters enable solutions to be generated within acceptable timeframes for real-time applications.