## 5 Experimental Results and Analysis
### Experiment 3: POM-PSASIDEA Algorithm Performance Analysis
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Instances**} & \multicolumn{3}{c}{**POM-PSASIDEA**} & \multicolumn{3}{c}{**AGDE-MPP**} & \multicolumn{3}{c}{**A-MPMO**} \\ \cline{2-10}  & \(f_{\text{max}}\) & \(f_{avg}\) & \(f_{\text{min}}\) & \(f_{\text{max}}\) & \(f_{\text{avg}}\) & \(f_{\text{min}}\) & \(f_{\text{max}}\) & \(f_{avg}\) & \(f_{\text{min}}\) \\ \hline LA\_100 & 479.6 & 465.6 & 447.8 & 446 & 415.7 & 404.5 & 417.6 & 401.5 & 382.8 \\ LA\_150 & 778.1 & 756.6 & 728.7 & 728.1 & 678.9 & 659.2 & 667.1 & 654 & 621.9 \\ LA\_200 & 1032.2 & 1015.2 & 983.7 & 967.6 & 906.5 & 882.5 & 924.9 & 880.9 & 841.6 \\ LA\_250 & 1346.0 & 1325.6 & 1290.9 & 1264.3 & 1180.9 & 1151.6 & 1222.5 & 1142.5 & 1095.8 \\ LA\_300 & 1681.2 & 1659.7 & 1621.7 & 1571.6 & 1468.3 & 1432.9 & 1578.2 & 1434.7 & 1372.3 \\ LA\_350 & 1995.6 & 1970.3 & 1916.7 & 1865.9 & 1742.8 & 1698.2 & 1734.7 & 1700.7 & 1617.7 \\ \hline \multirow{2}{*}{**Avg**} & **1218.7** & **1198.8** & **1164.9** & **1140.5** & **1065.5** & **1038.2** & **1090.8** & **1035.7** & **988.7** \\ GA\_100 & 473.2 & 458.1 & 445.2 & 442.2 & 414.8 & 405.8 & 428.8 & 397 & 380.3 \\ GA\_150 & 759.1 & 737.1 & 721.6 & 707.2 & 663.2 & 646 & 696.7 & 636.7 & 610.7 \\ GA\_200 & 1059.0 & 1041.0 & 1012.7 & 988 & 921.8 & 902.9 & 911.3 & 902.3 & 859 \\ GA\_250 & 1378.0 & 1354.7 & 1299.9 & 1285.7 & 1199.1 & 1169.5 & 1205.9 & 1179.5 & 1127.8 \\ GA\_300 & 1685.3 & 1662.5 & 1608.4 & 1570.3 & 1462.3 & 1420.1 & 1524.4 & 1432.4 & 1375.1 \\ GA\_350 & 2013.2 & 1983.6 & 1934.0 & 1886.4 & 1767.4 & 1721.7 & 1782.3 & 1713.9 & 1640.7 \\ \hline \multirow{2}{*}{**Avg**} & **1227.9** & **1206.1** & **1170.3** & **1146.6** & **1071.4** & **1044.3** & **1091.6** & **1043.6** & **998.9** \\ \cline{2-10}  & \multicolumn{3}{c}{**APSDE**} & \multicolumn{3}{c}{**ADECSA**} & \multicolumn{3}{c}{**SLPS-ADE**} \\ \cline{2-10}  & \(f_{\text{max}}\) & \(f_{avg}\) & \(f_{\text{min}}\) & \(f_{\text{max}}\) & \(f_{avg}\) & \(f_{\text{min}}\) & \(f_{\text{max}}\) & \(f_{avg}\) & \(f_{\text{min}}\) \\ \hline LA\_100 & 401.8 & 393.9 & 374.8 & 370.2 & 359.4 & 348.9 & 350.4 & 345.2 & 335.8 \\ LA\_150 & 697.2 & 645.6 & 618.8 & 614.1 & 584.9 & 571.7 & 573.1 & 565.6 & 550.8 \\ LA\_200 & 931.3 & 854.4 & 817.5 & 851.7 & 781.4 & 764.8 & 771.4 & 763.6 & 747.7 \\ LA\_250 & 1189.7 & 1113.7 & 1062.8 & 1093.3 & 1021.8 & 997.5 & 992.6 & 998.2 & 978.2 \\ LA\_300 & 1476.2 & 1392.6 & 1332.3 & 1342.5 & 1266.5 & 1232 & 1234.1 & 1234.7 & 1199.6 \\ LA\_350 & 1794.4 & 1646.3 & 1567 & 1540 & 1509.6 & 1472.3 & 1492 & 1480 & 1441 \\ \hline \multirow{2}{*}{**Avg**} & **1081.8** & **1007.8** & **962.2** & **968.6** & **920.6** & **897.9** & **902.3** & **897.9** & **875.5** \\ \cline{2-10}  & 411.3 & 391.7 & 374.1 & 389.4 & 360.6 & 350.6 & 341 & 342.3 & 334.7 \\ GA\_150 & 631.4 & 623.2 & 593.3 & 586 & 574.4 & 557.8 & 546.1 & 554 & 537.4 \\ GA\_200 & 907.1 & 872.2 & 831.2 & 833.8 & 801.7 & 780.8 & 738.8 & 763.5 & 741.6 \\ GA\_250 & 1170.5 & 1136.4 & 1088.6 & 1053 & 1042.6 & 1018.4 & 1022.3 & 1001.9 & 973.8 \\ GA\_300 & 1400.5 & 1385.7 & 1319.8 & 1396.1 & 1269.2 & 1231.1 & 1246.9 & 1224.2 & 1194.9 \\ GA\_350 & 1858.2 & 1689.3 & 1614.8 & 1599 & 1522.8 & 1481.7 & 1477.8 & 1454.5 & 1417.8 \\ \hline \multirow{2}{*}{**Avg**} & **1063.2** & **1016.4** & **970.3** & **976.2** & **928.6** & **903.4** & **903.0** & **890.1** & **866.7** \\ \cline{2-10}  & \multicolumn{3}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \\ \end{tabular}
\end{table} 
Table 5: Simulated experiment results of the POM-PSASIDEA algorithm in different instances. average fitness value \(f_{avg}\) reflects the fitness value level of the population as a whole, and the worst fitness value \(f_{\text{min}}\) reflects the existence of the worse solution for the population (or falling into the worst fitness value reflects whether the population has a poor solution or falls into local optimality).