# Multiprocessor Fair Scheduling Based on an Improved Slime Mold Algorithm
## 5 Performance Testing and Analysis of the Improved Slime Mold Algorithm
Through testing the algorithms on the four functions, it can be concluded that the WOA performs the worst and exhibits a convergence stagnation. The IMSMA achieves the best performance with the fastest convergence speed and a good ability to escape local optima. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Function** & **Algorithms** & **Average Fitness Value** & **Standard Deviation** & **Best Value** & **Worst Value** \\ \hline \multirow{5}{*}{F1} & WOA & 8.5850 \(\times\)\(10^{0}\) & 5.3670 \(\times\)\(10^{0}\) & 1.3076 \(\times\)\(10^{0}\) & 2.2033 \(\times\)\(10^{1}\) \\  & BOA & 1.2247 \(\times\)\(10^{-6}\) & 4.0456 \(\times\)\(10^{-7}\) & 3.7431 \(\times\)\(10^{-7}\) & 2.1905 \(\times\)\(10^{-6}\) \\  & SSA & 4.1472 \(\times\)\(10^{-5}\) & 0.0002 \(\times\)\(10^{0}\) & 1.5931 \(\times\)\(10^{-96}\) & 0.0010 \(\times\)\(10^{0}\) \\  & SMA & 3.2471 \(\times\)\(10^{-138}\) & 1.7486 \(\times\)\(10^{-137}\) & 2.9324 \(\times\)\(10^{-278}\) & 9.7413 \(\times\)\(10^{-137}\) \\  & IMSMA & **1.1214 \(\times\)\(\mathbf{10^{-295}}\)** & **0.0000 \(\times\)\(\mathbf{10^{0}}\)** & **0.0000 \(\times\)\(\mathbf{10^{0}}\)** & **3.3642 \(\times\)\(\mathbf{10^{-294}}\)** \\ \hline \multirow{5}{*}{F2} & WOA & 9.2021 \(\times\)\(10^{4}\) & 2.6686 \(\times\)\(10^{4}\) & 4.9993 \(\times\)\(10^{4}\) & 1.4113 \(\times\)\(10^{5}\) \\  & BOA & 5.5098 \(\times\)\(10^{-10}\) & 3.9426 \(\times\)\(10^{-11}\) & 4.7340 \(\times\)\(10^{-10}\) & 6.3100 \(\times\)\(10^{-10}\) \\  & SSA & 8.0274 \(\times\)\(10^{-8}\) & 3.2021 \(\times\)\(10^{-7}\) & **0.0000 \(\times\)\(\mathbf{10^{0}}\)** & 1.7711 \(\times\)\(10^{-6}\) \\  & SMA & 5.3711 \(\times\)\(10^{-241}\) & **0.0000 \(\times\)\(\mathbf{10^{0}}\)** & **0.0000 \(\times\)\(\mathbf{10^{0}}\)** & 1.6113 \(\times\)\(10^{-239}\) \\  & IMSMA & **0.0000 \(\times\)\(\mathbf{10^{0}}\)** & **0.0000 \(\times\)\(\mathbf{10^{0}}\)** & **0.0000 \(\times\)\(\mathbf{10^{0}}\)** & **0.0000 \(\times\)\(\mathbf{10^{0}}\)** \\ \hline \multirow{5}{*}{F3} & WOA & 2.5141 \(\times\)\(10^{1}\) & 2.5713 \(\times\)\(10^{1}\) & 1.5288 \(\times\)\(10^{0}\) & 1.0146 \(\times\)\(10^{2}\) \\  & BOA & 1.2962 \(\times\)\(10^{-9}\) & 2.4117 \(\times\)\(10^{-10}\) & 9.6035 \(\times\)\(10^{-10}\) & 2.0675 \(\times\)\(10^{-9}\) \\  & SSA & 4.2729 \(\times\)\(10^{-10}\) & 1.5760 \(\times\)\(10^{-10}\) & **0.0000 \(\times\)\(\mathbf{10^{0}}\)** & 7.3477 \(\times\)\(10^{-9}\) \\  & SMA & **0.0000 \(\times\)\(\mathbf{10^{0}}\)** & **0.0000 \(\times\)\(\mathbf{10^{0}}\)** & **0.0000 \(\times\)\(\mathbf{10^{0}}\)** & **0.0000 \(\times\)\(\mathbf{10^{0}}\)** \\  & IMSMA & **0.0000 \(\times\)\(\mathbf{10^{0}}\)** & **0.0000 \(\times\)\(\mathbf{10^{0}}\)** & **0.0000 \(\times\)\(\mathbf{10^{0}}\)** & **0.0000 \(\times\)\(\mathbf{10^{0}}\)** \\ \hline \multirow{5}{*}{F4} & WOA & 2.4084 \(\times\)\(10^{2}\) & 8.0646 \(\times\)\(10^{1}\) & 6.5122 \(\times\)\(10^{1}\) & 3.4340 \(\times\)\(10^{2}\) \\  & BOA & 1.1291 \(\times\)\(10^{2}\) & 8.6531 \(\times\)\(10^{1}\) & 8.1465 \(\times\)\(10^{-9}\) & 2.0361 \(\times\)\(10^{2}\) \\ \cline{1-1}  & SSA & 5.8915 \(\times\)\(10^{-7}\) & 2.5701 \(\times\)\(10^{-6}\) & **0.0000 \(\times\)\(\mathbf{10^{0}}\)** & 1.4265 \(\times\)\(10^{-5}\) \\ \cline{1-1}  & SMA & **0.0000 \(\times\)\(\mathbf{10^{0}}\)** & **0.0000 \(\times\)\(\mathbf{10^{0}}\)** & **0.0000 \(\times\)\(\mathbf{10^{0}}\)** & **0.0000 \(\times\)\(\mathbf{10^{0}}\)** \\ \cline{1-1}  & IMSMA & **0.0000 \(\times\)\(\mathbf{10^{0}}\)** & **0.0000 \(\times\)\(\mathbf{10^{0}}\)** & **0.0000 \(\times\)\(\mathbf{10^{0}}\)** & **0.0000 \(\times\)\(\mathbf{10^{0}}\)** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison table of algorithms’ test results.