Research Article

Dynamic Multi-Swarm Differential Learning Quantum Bird Swarm Algorithm and Its Application in Random Forest Classification Model

Table 9

Comparison on 8 multimodal functions with popular algorithms (Dim = 10, FEs = 100000).

FunctionTermGWOWOASCAGOASSADMSDL-QBSA

Max2.9544E + 032.9903E + 032.7629E + 032.9445E + 033.2180E + 033.0032E + 03
Min1.3037E + 038.6892E − 041.5713E + 031.3438E + 031.2839E − 041.2922E + 03
Mean1.6053E + 031.4339E + 011.7860E + 031.8562E + 032.5055E + 021.2960E + 03
Var2.6594E + 021.2243E + 021.6564E + 025.1605E + 023.3099E + 025.8691E + 01

Max1.3792E + 021.2293E + 021.2313E + 021.1249E + 023.2180E + 031.8455E + 01
Min0001.2437E + 011.2839E − 040
Mean4.4220E − 011.1252E + 009.9316E + 002.8378E + 012.5055E + 023.2676E − 03
Var4.3784E + 006.5162E + 001.9180E + 011.6240E + 013.3099E + 021.9867E − 01

Max2.0257E + 012.0043E + 011.9440E + 011.6623E + 013.2180E + 031.9113E + 00
Min4.4409E − 153.2567E − 153.2567E − 152.3168E + 001.2839E − 048.8818E − 16
Mean1.7200E − 024.2200E − 028.8870E − 015.5339E + 002.5055E + 023.1275E − 04
Var4.7080E − 016.4937E − 013.0887E + 002.8866E + 003.3099E + 022.2433E − 02

Max1.5246E + 021.6106E + 021.1187E + 026.1505E + 013.2180E + 033.1560E − 01
Min3.3000E − 03002.4147E − 011.2839E − 040
Mean4.6733E − 029.3867E − 021.2094E + 003.7540E + 002.5055E + 023.3660E − 05
Var1.9297E + 002.6570E + 006.8476E + 004.1936E + 003.3099E + 023.1721E − 03

Max9.5993E + 079.9026E + 075.9355E + 076.1674 E + 063.2180E + 036.4903E − 01
Min3.8394E − 091.1749E − 089.6787E − 031.8099E − 041.2839E − 044.7116E − 32
Mean1.2033E + 043.5007E + 044.8303E + 051.0465E + 042.5055E + 028.9321E − 05
Var9.8272E + 051.5889E + 064.0068E + 061.9887E + 053.3099E + 026.8667E − 03

Max2.2691E + 082.4717E + 081.1346E + 082.8101E + 073.2180E + 031.6407E − 01
Min3.2467E − 024.5345E − 081.1922E − 013.5465E − 051.2839E − 041.3498E − 32
Mean2.9011E + 044.3873E + 046.5529E + 057.2504E + 042.5055E + 026.7357E − 05
Var2.3526E + 062.7453E + 067.1864E + 061.2814E + 063.3099E + 022.4333E − 03

Max1.7692E + 011.7142E + 011.6087E + 018.7570E + 003.2180E + 031.0959E + 00
Min2.6210E − 070.0000E + 006.2663E − 1551.0497E − 021.2839E − 040
Mean1.0133E − 023.9073E − 015.9003E − 012.4770E + 002.5055E + 021.5200E − 04
Var3.0110E − 019.6267E − 011.4701E + 001.9985E + 003.3099E + 021.1633E − 02

Max4.4776E + 014.3588E + 013.9095E + 011.7041E + 013.2180E + 036.5613E − 01
Min1.9360E − 019.4058E − 082.2666E − 013.9111E + 001.2839E − 041.4998E − 32
Mean2.1563E − 014.7800E − 029.8357E − 015.4021E + 002.5055E + 029.4518E − 05
Var5.8130E − 011.0434E + 003.0643E + 001.6674E + 003.3099E + 027.0251E − 03