## 3 Simulation Results  
Characteristics and Conditions of Scenarios Used in the Simulation and the Hardware Specifications of the Simulation Environment  
Simulations were conducted on a system equipped with an Intel Core i9 14 th Generation processor, 96 GB of RAM, and running on the Windows operating system. MATLAB 2023b was utilized as the primary software environment for implementing and running the simulations. The proposed dual-adaptive PV panel reconfiguration model includes as many fixed arrays as the number of rows and a single-adaptive panel array for each row. In this study, simulations were conducted with two different configuration scenarios for the three-row and four-row systems. The values of adaptive and fixed panel arrays for the simulations are given in Table 3.  
Table 4 provides a comprehensive analysis of the effects of population size, crossover rate, and mutation rate on various metrics such as population diversity, best fitness, and convergence time in different experimental settings. The results show that higher population sizes and mutation rates tend to increase population diversity and facilitate the \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Number of**} & \multirow{2}{*}{**Configuration**} & \multicolumn{2}{c}{**Adaptive Part 1**} & \multicolumn{2}{c}{**Fixed Part**} & \multicolumn{2}{c}{**Adaptive Part 2**} \\  & & **(I\({}_{\text{scn}}\) (A))** & **(I\({}_{\text{scn}}\) (A))** & **(I\({}_{\text{scn}}\) (A))** \\ \cline{3-5}  & & \(A_{1}\) & \(A_{2}\) & \(A_{3}\) & \(F_{1}\) & \(F_{2}\) & \(F_{3}\) \\ \hline \multirow{2}{*}{3-Array} & Scenario 1 & \multirow{2}{*}{Scenario 2} & [1.51, 1.51, 1.7] & [7, 9.06, 10.04] & [3.41, 3.79, 3.79] \\  & Scenario 2 & & [3.79, 3.79, 2.27] & [10.99, 9.41, 8.49] & [3.79, 2.65, 1.89] \\ \hline \multirow{2}{*}{4-Array} & Scenario 3 & \multirow{2}{*}{Scenario 4} & **A\({}_{1}\)** & **A\({}_{2}\)** & **A\({}_{3}\)** & **A\({}_{4}\)** & **A\({}_{5}\)** \\  & Scenario 3 & & [1.13, 1.89, 2.65, 3] & [6.81, 7.92, 10.2, 11.37] & [2.65, 3.22, 3.79, 3.79] \\ \cline{1-1}  & Scenario 4 & & [3.79, 3.79, 3, 1.89] & [11.18, 10.2, 5.48, 4.72] & [3, 2.27, 1.13, 1.13] \\ \hline \hline \end{tabular}
\end{table} 
Table 3: PV array configuration scenarios used in the simulations. exploration of the solution space. Despite changes in experimental conditions, the algorithm consistently reaches the same best fitness value (0.17), demonstrating its robustness and reliability in determining the global optimum. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Population** & **Crossover** & **Mutation** & **Population** & **Best** & **Convergence** & **Optimum Configuration** & **Expr. ** \\
**Number** & **Rate** & **Rate** & **Diversity** & **Fitness (CVI)** & **Time (s)** & **(F: Fixed, A: Adaptive Panels)** & **Number** \\ \hline \multirow{6}{*}{50} & & 0.04 & 0.380408163 & 0.17 & 7.6378595 & F1-\(\lambda\)A2A3A5 F2-\(\lambda\)A1A4 F3-\(\lambda\)A6 & 1 \\  & & 0.06  
The convergence time shows significant variability depending on the parameter settings, with lower mutation rates leading to longer convergence times. In particular, configurations with larger populations and moderate mutation rates achieved the shortest convergence times, highlighting the importance of balanced parameter tuning. Additionally, the observed changes in the optimal configurations highlight the flexibility of the algorithm and its adaptability to dynamic simulation conditions, allowing the consistent optimization of PV panel configurations. _Best Fitness (CVI)_: It is seen that the best fitness values are the same as 0.17 in all simulations. This situation emphasizes the ability of the algorithm to reach a global optimum value in the solution space. This fixed optimum value shows that the algorithm reaches the targeted optimum stably despite different parameter values. _Convergence Time:_ In terms of convergence time, the relationship between population size, crossover rate and mutation rate is remarkable.