###### Abstract
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Run No.**} & \multirow{2}{*}{\(A\)} & \multirow{2}{*}{\(B\)} & \multirow{2}{*}{\(C\)} & \multicolumn{2}{c}{_TA_} & \multicolumn{2}{c}{_SR_} \\ \cline{5-8}  & & & & & \(\quad\) & \(\quad\) & \(\quad\) & \(\quad\) \\ \cline{5-8}  & & & & & \(\quad\) & \(\quad\) & \(\quad\) & \(\quad\) \\ \hline
1 & 3 & 3 & 3 & 3 & 34 & 32 & 68 & 64 \\
2 & 3 & 4 & 4 & 32 & 33 & 64 & 66 \\
3 & 3 & 5 & 5 & 31 & 31 & 62 & 62 \\
4 & 3 & 6 & 6 & 32 & 26 & 64 & 52 \\
5 & 4 & 3 & 4 & 34 & 32 & 68 & 64 \\
6 & 4 & 4 & 3 & 43 & 36 & 86 & 72 \\
7 & 4 & 5 & 6 & 30 & 30 & 60 & 60 \\
8 & 4 & 6 & 5 & 29 & 28 & 58 & 56 \\
9 & 5 & 3 & 5 & 32 & 30 & 64 & 60 \\
10 & 5 & 4 & 6 & 33 & 33 & 66 & 66 \\
11 & 5 & 5 & 3 & 36 & 28 & 72 & 56 \\
12 & 5 & 6 & 4 & 29 & 29 & 58 & 58 \\
13 & 6 & 3 & 6 & 32 & 31 & 64 & 62 \\
14 & 6 & 4 & 5 & 32 & 32 & 64 & 68 \\
15 & 6 & 5 & 4 & 28 & 28 & 56 & 56 \\
16 & 6 & 6 & 3 & 30 & 30 & 60 & 60 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: L16 orthogonal array. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**NA**} & \multicolumn{8}{c}{**Bin Number in \(l_{th}\) Position**} \\ \cline{2-13}  & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **11** & **12** \\ \hline \(NA_{l}\) & 12 & 12 & 0 & 0 & 8 & 0 & 8 & 0 & 0 & 0 & 3 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Number of assemblies. The significance of the number of bins of each part to maximize the number of assemblies is thoroughly analyzed using the following statistical analyses. The Pareto chart and residual plot are given in Figure 4a,b, respectively. The influence of the number of bins of different parts on making the number of successful assemblies is clearly understood from these figures. From the spatial distribution (Figure 4c), it is concluded that the range of the number of bins for each part is appropriately selected. The probability plot is given in Figure 4d. It is used to verify the statistical significance of the number of bins of parts \(A\), \(B\), and \(C\) in increasing the success rate of making assemblies. The surface plots indicating the relationship between the number of total assemblies (_TA_) and the number of bins of parts \(A\), \(B\), and \(C\) are given in Figure 5. It is understood from Figure 5a that the _TA_ value is gradually increased when increasing the number of bins of part \(A\). However, when increasing the number of bins of part \(B\), the _TA_ value is gradually decreased. Figure 5b depicts that the _TA_ value is slightly increased initially and then decreased while  
Figure 4: Statistical analyses of the results of the MFO algorithm. Figure 5: Surface plots. Furthermore, the quadratic type multiple linear regression model (MLRM) for the number of assemblies is formulated using Minitab(tm) software based on the relation between the number of bins of parts \(A\), \(B\), and \(C\) and the output response as success rate. The MLRM is given in Equation (9). \[N=7.5+16.92\ A+15.19\ B-15.94\ C-0.937\ A*A-0.812\ B*B+1.437\ C*C-2.034\ A*B \tag{9}\]  
Furthermore, in view of analyzing the statistical difference between the results of the MFO and HS algorithm, the paired \(t\)-test is conducted using Minitab software. The results such as descriptive statistics, estimation of paired difference, and the testing of significant difference are presented in Tables 12-14, respectively. The \(p\)-value from this test is less than 0.05, which stated the significant difference between MFO and HS algorithm. The convergence plot for both the MFO and HS algorithms based on the \(SR\) value is presented in Figure 7. From the plot, it is concluded that the MFO algorithm is quickly converged with a greater \(SR\) value when compared to the HS algorithm.