Table 33 Exploration/exploitation analysis of GWO-TLBO for CEC-2022.

From: An integrative TLBO-driven hybrid grey wolf optimizer for the efficient resolution of multi-dimensional, nonlinear engineering problems

Function name

Exploration rate

Exploitation rate

Best fitness value

Average fitness value

Standard deviation of fitness value

F1

0.47

0.53

9612.660101

9612.660101

34.39601145

F2

0.78

0.22

465.3424412

465.3424412

56.17916142

F3

0.70

0.30

604.6417547

604.6417547

50.66444084

F4

0.55

0.45

859.1150019

859.1150019

39.7236367

F5

0.42

0.58

1228.083774

1228.083774

30.18350778

F6

0.54

0.46

16,833.37468

16,833.37468

38.88188625

F7

0.55

0.45

2045.461874

2045.461874

41.82364354

F8

0.61

0.39

2227.385672

2227.385672

52.72607851

F9

0.92

0.08

2483.825282

2483.825282

68.6656569

F10

0.76

0.24

4322.38019

4322.38019

55.60364774

F11

0.58

0.42

3495.395375

3495.395375

40.64550639

F12

1.00

0.00

2965.454822

2965.454822

72.08404961