Table 1 Set parameters of the utilized algorithms.

From: Optimizing electric load forecasting with support vector regression/LSTM optimized by flexible Gorilla troops algorithm and neural networks a case study

SSA20

\(\:{N}_{fs}\)

5

\(\:{G}_{c}\)

2

\(\:{P}_{dp}\)

0.2

BOA21

No. of pockets

20

\(\:w\)

0.6

\(\:ES\)

0.2

BBO22

Habitat modification probability

1

Immigration probability bounds per gene

0.5

Step size for numerical integration of probabilities

1

Max immigration (I) and Max emigration (E)

1

Mutation probability

0.002

LS23

F

0.4

L

1

g

18

EPO25

\(\:\overrightarrow{A}\)

1

Temperature value (\(\:{T}^{{\prime\:}}\))

150

\(\:M\)

2

\(\:f\)

2.6

S

1.2

\(\:l\)

1.8