## 5 Results and Discussion
### Ten Random Results for Different Algorithms  
Table 4 shows the 10 random results for the different evolutionary algorithms based on normal years, wet years, and dry years. The average generated hydropower energy for the CA based on normal years was 106.20 (10\({}^{8}\) kwh), with values of 105.23, 103.98, and 104.22 (10\({}^{8}\) kwh) via the MBF, GA, and PSO, respectively. The average generated energy for normal years based on the CA was 0.94%, 2.09%, and 1.8% greater than via the MBF, GA, and PSO, respectively. Additionally, the computational time for the CA based on normal years was 24 s, which was 17%, 25%, and 31% less than via the MBF, PSO, and GA, respectively. It can be observed that the results for the dry and wet years assured that the CA could achieve a better value for the objective function with a minimal computational time. The computational time for the CA based on dry years was 23 s, which was 11.5%, 20%, and 28% less than via the MBF, GA, and PSO, respectively. The lowest computation time consumed refers to fast convergence: Also, this reduction occurred when the number of evaluations for the CA was less than other algorithms. The variation coefficient for the CA for all years was a small value, which proved that one computer run was reliable and proved the solution to be of high quality. The used computerized system for this research and the one used in Reference [26] by the authors are similar systems (PCs with i5 CPU 2.4 GHz, ram/500 GB HDD). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{6}{c}{**Dry Year**} \\ \hline
**Population** & **Objective Function** & \(\textit{fl}^{\textit{i}}\textit{iter}\) & **Objective Function** & \(\textit{AP}^{\textit{i}}\textit{iter}\) & **Objective Function** \\
**Size** & **(10\({}^{8}\) kwh)** & \(\textit{fl}^{\textit{i}}\textit{iter}\) & **(10\({}^{8}\) kwh)** & \(\textit{AP}^{\textit{i}}\textit{iter}\) & **Objective Function** \\ \hline
20 & 101.78 & 1 & 100.45 & 0.10 & 100.67 \\
40 & 102.54 & 2 & 101.23 & 0.20 & 101.11 \\
60 & 101.99 & 3 & 102.54 & 0.30 & 102.54 \\
80 & 101.01 & 4 & 101.01 & 0.40 & 100.02 \\ \hline \hline \multicolumn{6}{c}{**Normal Year**} \\ \hline
**Population** & **Objective Function** & \(\textit{fl}^{\textit{i}}\textit{iter}\) & **Objective Function** & \(\textit{AP}^{\textit{i}}\textit{iter}\) & **Objective Function** \\
**Size** & **(10\({}^{8}\) kwh)** & \(\textit{fl}^{\textit{i}}\textit{iter}\) & **(10\({}^{8}\) kwh)** & \(\textit{AP}^{\textit{i}}\textit{iter}\) & **Objective Function** \\ \hline
20 & 124.45 & 1 & 123.69 & 0.10 & 123.67 \\
40 & 125.78 & 2 & 124.58 & 0.20 & 125.78 \\
60 & 124.69 & 3 & 125.78 & 0.30 & 124.58 \\
80 & 123.12 & 4 & 124.49 & 0.40 & 124.49 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Sensitivity analysis for different algorithms for dry years, normal years, and wet years. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{3}{c}{**Dry Year**} \\ \hline
**Run** & **CA (10\({}^{8}\) kwh)** & **MBF (10\({}^{8}\) kwh)** & **GA (10\({}^{8}\) kwh)** & **PSO (10\({}^{8}\) kwh)** \\  & & **[**26**]** & **[**26**]** & **[**26**]** \\ \hline
1 & 102.54 & 101.45 & 97.84 & 100.20 \\ \hline
2 & 102.53 & 101.44 & 98.45 & 100.24 \\ \hline
3 & 102.53 & 101.45 & 98.72 & 100.18 \\
4 & 102.54 & 101.45 & 98.37 & 100.26 \\
5 & 102.54 & 101.45 & 98.97 & 100.23 \\
6 & 102.54 & 101.45 & 99.19 & 100.23 \\
7 & 102.54 & 101.45 & 99.29 & 100.33 \\
8 & 102.54 & 101.45 & 99.04 & 100.22 \\
9 & 102.54 & 101.45 & 99.10 & 100.21 \\
10 & 102.54 & 101.45 & 99.07 & 100.19 \\ \hline Average & 102.54 & 101.45 & 98.06 & 100.18 \\ \hline Variation coefficient & 0.0003 & 0.00003 & 0.716 & 0.065 \\ \hline Time (s) & 23 & 26 & 32 & 29 \\ \hline \hline \multicolumn{4}{c}{**Normal Year**} \\ \hline
**Run** & **CA (10\({}^{8}\) kwh)** & **MBF (10\({}^{8}\) kwh)** & **GA (10\({}^{8}\) kwh)** & **PSO (10\({}^{8}\) kwh)** \\  & & **[PERSON] et al. [26]** & **[PERSON] et al.