Table 3 Statistical results obtained from different algorithms on CEC 2019 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

1.0000E+00

4.4360E+00

1.8642E+00

1.4672E+01

1.0438E+00

std

5.3101E−10

4.1999E−02

2.5608E−01

2.3899E+00

1.4185E−02

GA

avg

3.2648E+05

4.5220E+02

7.6939E+00

1.1665E+02

1.4314E+00

std

1.1632E+05

3.4891E+01

5.6725E−02

1.2830E+01

5.4219E−02

PSO

avg

1.3819E+07

9.0960E+02

1.0444E+01

2.4056E+02

1.9162E+00

std

2.1059E+07

3.2540E+02

5.6372E−01

1.7613E+02

2.0756E−01

SCA

avg

2.2568E+05

2.6646E+02

5.1290E+00

1.2443E+02

1.5491E+00

std

3.0806E+05

6.7367E+01

1.0833E+00

2.4558E+01

9.7463E−02

MFO

avg

3.3762E+08

2.1620E+03

1.0816E+01

8.6194E+03

3.9494E+00

std

1.3608E+08

5.6290E+02

4.8941E−01

3.4470E+03

6.7227E−01

ALO

avg

2.5662E+06

4.2218E+02

5.0445E+00

3.0418E+01

1.3248E+00

std

2.0764E+06

2.1465E+02

2.4198E+00

1.2777E+01

2.0214E−01

MVO

avg

1.6838E+06

2.6842E+02

7.5775E+00

2.9282E+01

1.1843E+00

std

1.5459E+06

1.3917E+02

1.8665E+00

1.0332E+01

8.2334E−02