## 5 Computational Results
\begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Input instance**} & \multicolumn{5}{c}{**Fitness value**} & \multicolumn{5}{c}{**Execution time (min)**} & **Success** \\ \cline{2-11}
**BCV3-46.2** & **Best** & **Worst** & **Average** & **STD** & **CV (\%)** & **Best** & **Worst** & **Average** & **STD** & **rate** \\ \hline PowSIZE = 1 & 894 & 894 & 894 & 0 & 0 & 16 & 48 & 28 & 8.71 & 100\% \\ PowSIZE = 2 & 894 & 894 & 894 & 0 & 0 & 16 & 31 & 25 & 4.37 & 100\% \\ PowSIZE = 3 & 894 & 894 & 894 & 0 & 0 & 16 & 38 & 28 & 7.64 & 100\% \\ PowSIZE = 4 & 894 & 894 & 894 & 0 & 0 & 17 & 43 & 34 & 8 & 100\% \\ PowSIZE = 5 & 894 & 894 & 894 & 0 & 0 & 25 & 42 & 33 & 5.55 & 100\% \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Investigating the effect of population size for input instance, BCV3-46.2. As shown in Table 12, setting the value of population size equal to 2 assists the algorithm in achieving the lowest best and average fitness values in the lowest best and average execution times, respectively. As shown in Table 13, setting the value of swapping probability equal to 0.97 assists the algorithm in achieving the lowest best and average fitness value and the highest success rate. In Tables 14-16, experimental results that investigate the effect of parameter setting to algorithms' performance and behavior regarding input instance, LLR, are presented. As shown in Table 14, setting the first phase's number of cycles equal to 1 assists the algorithm in achieving the lowest best and average fitness values and the highest success rate. Except for that, if we do not use the first phase at all (first phase cycles = 0), execution times and success rate are rather worse. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Input instance**} & \multicolumn{5}{c}{**Fitness value**} & \multicolumn{5}{c}{**Execution time (s)**} & \multicolumn{2}{c}{**Success**} \\ \cline{2-11}
**MUSA** & **Best** & **Worst** & **Average** & **STD** & **CV (\%)** & **Best** & **Worst** & **Average** & **STD** & **rate** \\ \hline Swapping probability & & & & & & & & & & \\ \(=0.85\) & 175 & 185 & 176.5 & 3.38 & 1.9 & 0.14 & 0.42 & 0.32 & 0.14 & 80\% \\ Swapping probability & & & & & & & & & & \\ \(=0.9\) & 175 & 185 & 176.5 & 3.38 & 1.9 & 0.12 & 0.38 & 0.2 & 0.1 & 80\% \\ Swapping probability & & & & & & & & & & \\ \(=0.95\) & 175 & 185 & 176 & 3.16 & 1.79 & 0.11 & 0.29 & 0.18 & 0.07 & 90\% \\ Swapping probability & & & & & & & & & & \\ \(=0.97\) & 175 & 175 & 0 & 0 & 0.12 & 0.52 & 0.22 & 0.13 & 100\% \\ Swapping probability & & & & & & & & & & \\ \(=0.99995\) & & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Investigating the effect of swapping probability for input instance, MUSA. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Input instance**} & \multicolumn{5}{c}{**Fitness value**} & \multicolumn{5}{c}{**Execution time (s)**} & \multicolumn{2}{c}{**Success**} \\ \cline{2-11}
**MUSA** & **Best** & **Worst** & **Average** & **STD** & **CV (\%)** & **Best** & **Worst** & **Average** & **STD** & **rate** \\ \hline POPSIZE = 1 & 175 & 180 & 175.65 & 1.1 & 0.62 & 0.31 & 0.54 & 0.4 & 0.07 & 95\% \\ POPSIZE = 2 & 175 & 175 & 175 & 0 & 0 & 0.12 & 0.52 & 0.22 & 0.13 & 100\% \\ POPSIZE = 3 & 175 & 175 & 175 & 0 & 0 & 0.51 & 0.74 & 0.6 & 0.12 & 100\% \\ POPSIZE = 4 & 175 & 175 & 175 & 0 & 0 & 0.19 & 0.82 & 0.5 & 0.24 & 100\% \\ POPSIZE = 5 & 175 & 175 & 175 & 0 & 0 & 0.43 & 0.75 & 0.58 & 0.11 & 100\% \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Investigating the effect of population size for input instance, MUSA. As shown in Table 15, setting the value of population size equal to 2 assists the algorithm to achieve the lowest best and average fitness values and the highest success rate.