Research Article

A Dynamic Opposite Learning Assisted Grasshopper Optimization Algorithm for the Flexible JobScheduling Problem

Table 4

The mean and standard value of unimodal/multimodal test functions.

AlgorithmsDOLGOAGOAGWOTLBOJaya

F1Mean1.11E + 061.17E + 075.71E + 071.27E + 084.70E + 06
Std4.32E + 069.91E + 063.26E + 078.84E + 071.27E + 06
F2Mean5.66E + 033.79E + 031.73E + 092.25E + 104.73E + 08
Std4.38E + 032.21E + 039.04E + 086.61E + 091.43E + 08
F3Mean2.75E − 024.30E + 022.13E + 043.63E + 042.87E + 04
Std2.28E − 021.89E + 027.27E + 035.82E + 034.28E + 03
F4Mean1.05E + 021.04E + 023.09E + 022.57E + 032.12E + 02
Std6.07E + 016.55E + 011.84E + 021.53E + 031.40E + 01
F5Mean2.00E + 012.00E + 012.09E + 012.04E + 012.09E + 01
Std9.33E − 041.95E − 066.04E − 021.43E − 015.19E − 02
F6Mean1.59E + 011.71E + 011.29E + 012.69E + 012.24E + 01
Std2.09E + 005.52E + 003.85E + 003.89E + 004.18E + 00
F7Mean2.91E − 021.32E − 027.38E + 002.61E + 025.68E + 00
Std3.15E − 021.51E − 024.22E + 008.86E + 011.76E + 00
F8Mean1.10E + 021.10E + 028.41E + 011.91E + 021.83E + 02
Std3.50E + 012.65E + 011.48E + 013.18E + 011.45E + 01
F9Mean1.06E + 026.50E + 017.82E + 012.16E + 021.84E + 02
Std1.95E + 012.40E + 018.74E − 022.21E + 011.60E + 01
F10Mean2.45E + 033.49E + 032.09E + 035.02E + 035.03E + 03
Std6.29E + 023.02E + 026.41E + 023.65E + 028.82E + 02
F11Mean2.85E + 032.96E + 032.09E + 035.04E + 036.23E + 03
Std6.49E + 025.70E + 026.25E + 023.71E + 024.48E + 02
F12Mean1.65E − 015.26E − 011.87E + 001.13E + 002.39E + 00
Std7.88E − 022.47E − 011.03E + 003.46E − 012.71E − 01
F13Mean3.92E − 014.21E − 014.21E − 013.88E + 005.89E − 01
Std9.92E − 028.89E − 027.52E − 024.79E − 011.01E − 01
F14Mean2.29E − 012.21E − 011.03E + 019.67E + 012.79E − 01
Std6.40E − 022.98E − 021.62E + 011.52E + 012.10E − 02
F15Mean7.03E + 006.87E + 002.21E + 018.24E + 032.20E + 01
Std2.04E + 009.11E − 019.62E + 008.24E + 031.75E + 00
F16Mean1.18E + 011.15E + 011.09E + 011.15E + 011.26E + 01
Std5.11E − 016.85E − 014.16E − 013.96E − 012.09E − 01
Best num57500