Table 4 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

F6

F7

F8

F9

F10

nSCA

avg

9.3824E+00

1.0001E+00

1.0000E+00

1.2184E+00

1.9098E+01

std

4.0091E−01

6.2581E−05

8.5380E−11

3.6162E−02

5.4587E+00

GA

avg

1.0651E+01

5.9839E+01

1.0119E+00

7.1370E+00

2.1416E+01

std

1.4966E−01

5.8617E+01

4.6964E−03

4.3181E−01

1.7170E−02

PSO

avg

1.1121E+01

6.8933E+01

1.0008E+00

3.0083E+00

2.1452E+01

std

5.1184E−01

8.7810E+01

9.1039E−04

3.2582E+00

2.6011E−01

SCA

avg

1.1069E+01

6.3272E+00

1.0020E+00

4.8350E+00

2.0601E+01

std

5.8813E−01

1.7654E+00

8.6689E−04

8.1033E−01

2.5256E+00

MFO

avg

1.0302E+01

6.0386E+02

1.2980E+00

5.7099E+02

2.1465E+01

std

1.2608E+00

1.4460E+02

1.1544E−01

2.0007E+02

1.1999E−01

ALO

avg

5.3504E+00

4.4431E+01

1.0000E+00

1.3973E+00

2.1038E+01

std

1.4560E+00

1.8339E+02

2.4321E−11

2.2292E−01

8.2668E−02

MVO

avg

7.4261E+00

6.1456E+01

1.0000E+00

1.5303E+00

2.1103E+01

std

1.2525E+00

2.0799E+02

8.7431E−07

1.6057E−01

4.3663E−02