Table 2 The values are set for the control parameters of the competitor algorithms.

From: Learning cooking algorithm for solving global optimization problems

\(\text {Algorithm}\)

\(\text {Parameter}\)

\(\text {Value}\)

\({\text {PSO}}\)

Cognitive constant (C1)

1.5

Social constant (C2)

2

Local constant (W)

0.3

\({\text {TSA}}\)

Parameter \(P_{min}\)

1

Parameter \(P_{max}\)

4

\({\text {SSA}}\)

Leader position update probability

0.5

\({\text {MVO}}\)

Wormhole existence probability (WEP)

\(WEP\_Max\) = 1, \(WEP\_Min\) = 0.2

\({\text {GWO}}\)

l is a random number

\([- 1, 1]\)

r is a random vector

[0, 1]

Convergence parameter (a)

Linear reduction from 2 to 0

\({\text {WOA}}\)

l is a random number

[– 1, 1]

r is a random vector

[0, 1]

Convergence parameter (a)

Linear reduction from 2 to 0

\({\text {GJO}}\)

Constant value

\(c_{1} = 1.5\)

Energy of the prey

\(E_{1}\) is linearly decreased from 1.5 to 0

\({\text {CMAES}}\)

Number of parents

\(\mu \)= \(\lfloor N/2 \rfloor \)

Step size

\(\alpha \)=2

\({\text {LSHADE}}\)

Pbest rate

0.11

Arc rate

1.4

Memory size

5

\({\text {IGWO}}\)

l is a random number

\([- 1,1]\)

r is a random vector

[0, 1]

Convergence parameter (a)

Linear reduction from 2 to 0

\({\text {MWOA}}\)

l is a random number

[– 1,1]

r is a random vector

[0, 1]

Convergence parameter (a)

Linear reduction from 2 to 0

\({\text {TLBO}}\)

Teacher factor

[1, 2]

Random number

[0, 1]

\({\text {MTBO}}\)

The scaling factors

\(Li=(0.25+0.25*rand) \)

\(Ai=(0.75+0.25*rand) \)

\(Mi=(0.75+0.25*rand) \)

\({\text {BWO}}\)

The probability of whale fall decreased at the interval \(W_{f}\)

[0.1, 0.05]

\({\text {HHO}}\)

Probability thresholds of escaping, escaping energy

0.5, 0.5

\({\text {MGO}}\)

The population size

\(N=30\)

Maximum number of iterations

\(T=1000\)

\({\text {SCSO}}\)

Sensitivity range \((r_{G})\)

[2, 0]

Phases control range (R)

\([- 2{r}_{G}, 2{r}_{G}]\)