Table 3 51 benchmark functions, U: Uni-modal, M: Multi-modal, S: Separable, N: Non-separable.

From: Learning cooking algorithm for solving global optimization problems

\({\text {Name}}\)

\({\text {Function}}\)

\({\text {Characteristics}}\)

\({\text {Dimension}}\)

\({\text {Range}}\)

\({\text {f}}_{\text {optimal}}\)

\({\text {F1}}\)

Sphere

US

30

[– 100, 100]

0

\({\text {F2}}\)

Schwefel’problem2.22

UN

30

[– 10, 10]

0

\({\text {F3}}\)

Schwefel’problem1.2

UN

30

[– 100, 100]

0

\({\text {F4}}\)

Schwefel’problem2.21

UN

30

[– 100, 100]

0

\({\text {F5}}\)

Rosen brock

UN

30

[– 30, 30]

0

\({\text {F6}}\)

Step

US

30

[– 100, 100]

0

\({\text {F7}}\)

Noise

US

30

[– 1.28, 1.28]

0

\({\text {F8}}\)

Generalized Schwefel’s problem

MS

30

[– 500, 500]

– 12569.5

\({\text {F9}}\)

Rastrigin

MS

30

[– 5.12, 5.12]

0

\({\text {F10}}\)

Ackley

MN

30

[– 32, 32]

0

\({\text {F11}}\)

Griewank

MN

30

[– 600, 600]

0

\({\text {F12}}\)

Generalized penalized function1

MN

30

[– 50, 50]

0

\({\text {F13}}\)

Generalized penalized function2

MN

30

[– 50, 50]

0

\({\text {F14}}\)

Shekel’s foxholes function

MS

2

[– 65, 65]

1

\({\text {F15}}\)

Kowalik’s function

MN

4

[– 5, 5]

0.00030

\({\text {F16}}\)

Six-hump camelback

MN

2

[– 5, 5]

– 1.0316

\({\text {F17}}\)

Branin

MS

2

[– 5, 5]

0.398

\({\text {F18}}\)

Goldstein-price function

MN

2

[– 2, 2]

3

\({\text {F19}}\)

Hartmann1

MN

3

[1, 3]

– 3.86

\({\text {F20}}\)

Hartmann2

MN

6

[0, 1]

– 3.32

\({\text {F21}}\)

Shekel1

MN

4

[0, 10]

– 10.1532

\({\text {F22}}\)

Shekel2

MN

4

[0, 10]

– 10.4028

\({\text {F23}}\)

Shekel3

MN

4

[0, 10]

– 10.5363

\({\text {F24}}\)

Stepint

US

5

[– 5.12, 5.12]

0

\({\text {F25}}\)

SumSquares

US

30

[– 10, 10]

0

\({\text {F26}}\)

Beale

UN

5

[– 4.5, 4.5]

0

\({\text {F27}}\)

Easom

UN

2

[– 100, 100]

– 1

\({\text {F28}}\)

Matyas

UN

2

[– 10, 10]

0

\({\text {F29}}\)

Colville

UN

4

[– 10, 10]

0

\({\text {F30}}\)

Trid6

UN

6

[\(-D^2\),\(D^2\)]

– 360

\({\text {F31}}\)

Trid10

UN

10

[\(-D^2\),\(D^2\) ]

– 2600

\({\text {F32}}\)

Zakharov

UN

10

[– 5, 10]

0

\({\text {F33}}\)

Powell

UN

24

[– 4, 5]

0

\({\text {F34}}\)

Dixon– Price

UN

30

[– 10, 10]

0

\({\text {F35}}\)

Bohachevsky1

MS

2

[– 100, 100]

0

\({\text {F36}}\)

Booth

MS

2

[– 10, 10]

0

\({\text {F37}}\)

Michalewicz2

MS

2

[0,\(\pi \)]

– 1.8013

\({\text {F38}}\)

Michalewicz5

MS

5

[0,\(\pi \)]

– 4.6877

\({\text {F39}}\)

Michalewicz10

MS

10

[0,\(\pi \)]

– 9.6602

\({\text {F40}}\)

Schaffer

MN

2

[– 100, 100]

0

\({\text {F41}}\)

Bohachevshy2

MN

2

[– 100, 100]

0

\({\text {F42}}\)

Bohachevshy3

MN

2

[– 100, 100]

0

\({\text {F43}}\)

Shubert

MN

2

[– 100, 100]

– 25

\({\text {F44}}\)

Perm

MN

4

[\(- D\),D]

0

\({\text {F45}}\)

PowerSum

MN

4

[0,D]

0

\({\text {F46}}\)

Langerman2

MN

2

[0, 10]

– 1.08

\({\text {F47}}\)

Langerman5

MN

5

[0, 10]

– 4.825

\({\text {F48}}\)

Langerman10

MN

10

[0, 10]

– 8.76

\({\text {F49}}\)

FletcherPowell2

MN

2

[\(-\pi \),\(\pi \)]

0

\({\text {F50}}\)

FletcherPowell5

MN

5

[\(-\pi \),\(\pi \)]

0

\({\text {F51}}\)

FletcherPowell10

MN

10

[\(-\pi \),\(\pi \)]

0