Table 6 Statistical results obtained from different algorithms on CEC 2021 test functions.

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

Algorithm/function

Statistical metrics

F1

F2

F3

F4

F5

nSCA

avg

2.6139E+06

9.7566E+04

2.2058E+04

1.9034E+03

6.1125E+03

std

3.8102E+06

7.6190E+04

1.7970E+04

8.7061E−01

1.4966E+03

GA

avg

1.0005E+08

5.1855E+09

3.0612E+09

1.9675E+03

2.4600E+04

std

1.7850E+08

1.3072E+10

1.0216E+10

7.3585E+01

9.1165E+03

PSO

avg

9.5844E+07

2.4241E+09

2.9732E+09

1.9891E+03

1.7675E+05

std

2.6058E+08

4.3878E+09

6.7726E+09

2.3611E+02

1.5460E+05

SCA

avg

3.3244E+07

2.1955E+09

4.6439E+08

1.9043E+03

1.5350E+04

std

1.5347E+07

9.4690E+08

2.1793E+08

6.9655E−01

7.1861E+03

MFO

avg

5.7981E+09

6.8269E+11

2.1017E+11

1.7674E+04

9.3913E+05

std

2.2265E+09

3.3572E+11

8.4213E+10

2.7271E+04

7.9781E+05

ALO

avg

2.0900E+03

3.2377E+05

7.5700E+04

1.9021E+03

4.3610E+04

std

2.2000E+03

3.8084E+05

7.5302E+04

1.1604E+00

3.9773E+04

MVO

avg

1.1209E+04

5.1402E+06

5.3232E+05

1.9022E+03

2.1333E+04

std

8.7490E+03

9.8699E+05

5.2583E+05

6.4910E−01

6.1424E+03