Table 7 Statistical results obtained from different algorithms on CEC 2021 test functions (continued).

From: Enhancing engineering optimization using hybrid sine cosine algorithm with Roulette wheel selection and opposition-based learning

Algorithm/function

Statistical metrics

F6

F7

F8

F9

F10

nSCA

avg

2.1239E+03

6.3754E+03

2.3024E+03

2.5968E+03

3.0088E+03

std

2.9213E+02

2.2236E+03

9.3241E−01

1.7917E+01

1.8385E+01

GA

avg

4.8594E+03

7.1530E+03

2.3345E+03

3.1832E+03

3.1595E+03

std

3.7275E+03

5.2281E+03

1.1242E+01

8.3419E+02

4.1794E+01

PSO

avg

7.2962E+03

1.5042E+05

2.3130E+03

3.0133E+03

3.0895E+03

std

5.0285E+03

5.4994E+05

4.4205E+00

3.3501E+02

6.3185E+01

SCA

avg

2.9617E+03

1.1761E+04

2.3087E+03

2.8274E+03

3.0072E+03

std

1.0790E+03

4.5072E+03

1.1630E+00

8.4929E+01

1.1181E+01

MFO

avg

2.9666E+04

7.2657E+06

2.3457E+03

5.5363E+03

3.4342E+03

std

1.7217E+04

6.1941E+06

9.3688E+00

6.0756E+02

2.1886E+02

ALO

avg

6.2181E+03

2.5595E+04

2.3075E+03

2.5974E+03

3.0110E+03

std

5.7222E+03

2.6519E+04

2.9237E+00

3.3996E+01

3.7818E+01

MVO

avg

2.4545E+03

2.3408E+03

2.3036E+03

2.6071E+03

2.9798E+03

std

2.1974E+03

2.3907E+02

1.8328E+00

5.2987E+01

2.5083E+01