# N-D Test Functions D¶

class go_benchmark.Damavandi(dimensions=2)

Damavandi test objective function.

This class defines the Damavandi global optimization problem. This is a multimodal minimization problem defined as follows:

Here, represents the number of dimensions and for .

Two-dimensional Damavandi function

Global optimum: for for

class go_benchmark.Deb01(dimensions=2)

Deb 1 test objective function.

This class defines the Deb 1 global optimization problem. This is a multimodal minimization problem defined as follows:

Here, represents the number of dimensions and for .

Two-dimensional Deb 1 function

Global optimum: . The number of global minima is that are evenly spaced in the function landscape, where represents the dimension of the problem.

class go_benchmark.Deb02(dimensions=2)

Deb 2 test objective function.

This class defines the Deb 2 global optimization problem. This is a multimodal minimization problem defined as follows:

Here, represents the number of dimensions and for .

Two-dimensional Deb 2 function

Global optimum: . The number of global minima is that are evenly spaced in the function landscape, where represents the dimension of the problem.

class go_benchmark.Decanomial(dimensions=2)

Decanomial test objective function.

This class defines the Decanomial function global optimization problem. This is a multimodal minimization problem defined as follows:

Here, represents the number of dimensions and for .

Two-dimensional Decanomial function

Global optimum: for

class go_benchmark.Deceptive(dimensions=2)

Deceptive test objective function.

This class defines the Deceptive global optimization problem. This is a multimodal minimization problem defined as follows:

Where is a fixed non-linearity factor; in this exercise, . The function is given by:

Here, represents the number of dimensions and for .

Two-dimensional Deceptive function

Global optimum: for for

class go_benchmark.DeckkersAarts(dimensions=2)

Deckkers-Aarts test objective function.

This class defines the Deckkers-Aarts global optimization problem. This is a multimodal minimization problem defined as follows:

Here, represents the number of dimensions and for .

Two-dimensional Deckkers-Aarts function

Global optimum: for

class go_benchmark.DeflectedCorrugatedSpring(dimensions=2)

DeflectedCorrugatedSpring test objective function.

This class defines the Deflected Corrugated Spring function global optimization problem. This is a multimodal minimization problem defined as follows:

Where, in this exercise, and .

Here, represents the number of dimensions and for .

Two-dimensional Deflected Corrugated Spring function

Global optimum: for for

class go_benchmark.DeVilliersGlasser01(dimensions=4)

DeVilliers-Glasser 1 test objective function.

This class defines the DeVilliers-Glasser 1 function global optimization problem. This is a multimodal minimization problem defined as follows:

Where, in this exercise, and .

Here, represents the number of dimensions and for .

Global optimum: for for .

class go_benchmark.DeVilliersGlasser02(dimensions=5)

DeVilliers-Glasser 2 test objective function.

This class defines the DeVilliers-Glasser 2 function global optimization problem. This is a multimodal minimization problem defined as follows:

Where, in this exercise, and .

Here, represents the number of dimensions and for .

Global optimum: for for .

class go_benchmark.DixonPrice(dimensions=2)

Dixon and Price test objective function.

This class defines the Dixon and Price global optimization problem. This is a multimodal minimization problem defined as follows:

Here, represents the number of dimensions and for .

Two-dimensional Dixon and Price function

Global optimum: for for

class go_benchmark.Dolan(dimensions=5)

Dolan test objective function.

This class defines the Dolan global optimization problem. This is a multimodal minimization problem defined as follows:

Here, represents the number of dimensions and for .

Global optimum: for

class go_benchmark.DropWave(dimensions=2)

DropWave test objective function.

This class defines the DropWave global optimization problem. This is a multimodal minimization problem defined as follows:

Here, represents the number of dimensions and for .

Two-dimensional DropWave function

Global optimum: for for

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