test_functions N-D Test Functions X

class XinSheYang01(dimensions=2)

Xin-She Yang 1 objective function.

This class defines the Xin-She Yang 1 global optimization problem. This is a multimodal minimization problem defined as follows:

f_{\text{XinSheYang01}}(x) = \sum_{i=1}^{n} \epsilon_i \lvert x_i
                             \rvert^i

The variable \epsilon_i, (i = 1, ..., n) is a random variable uniformly distributed in [0, 1].

Here, n represents the number of dimensions and x_i \in [-5, 5] for i = 1, ..., n.

XinSheYang01 function

Two-dimensional XinSheYang01 function


Global optimum: f(x) = 0 for x_i = 0 for i = 1, ..., n

Jamil, M. & Yang, X.-S. A Literature Survey of Benchmark Functions For Global Optimization Problems Int. Journal of Mathematical Modelling and Numerical Optimisation, 2013, 4, 150-194.


class XinSheYang02(dimensions=2)

Xin-She Yang 2 objective function.

This class defines the Xin-She Yang 2 global optimization problem. This is a multimodal minimization problem defined as follows:

f_{\text{XinSheYang02}}(x) = \frac{\sum_{i=1}^{n} \lvert{x_{i}}\rvert}
                              {e^{\sum_{i=1}^{n} \sin\left(x_{i}^{2.0}
                              \right)}}

Here, n represents the number of dimensions and x_i \in [-2\pi, 2\pi] for i = 1, ..., n.

XinSheYang02 function

Two-dimensional XinSheYang02 function


Global optimum: f(x) = 0 for x_i = 0 for i = 1, ..., n

Jamil, M. & Yang, X.-S. A Literature Survey of Benchmark Functions For Global Optimization Problems Int. Journal of Mathematical Modelling and Numerical Optimisation, 2013, 4, 150-194.


class XinSheYang03(dimensions=2)

Xin-She Yang 3 objective function.

This class defines the Xin-She Yang 3 global optimization problem. This is a multimodal minimization problem defined as follows:

f_{\text{XinSheYang03}}(x) = e^{-\sum_{i=1}^{n} (x_i/\beta)^{2m}}
                             - 2e^{-\sum_{i=1}^{n} x_i^2}
                             \prod_{i=1}^{n} \cos^2(x_i)

Where, in this exercise, \beta = 15 and m = 3.

Here, n represents the number of dimensions and x_i \in [-20, 20] for i = 1, ..., n.

XinSheYang03 function

Two-dimensional XinSheYang03 function


Global optimum: f(x) = -1 for x_i = 0 for i = 1, ..., n

Jamil, M. & Yang, X.-S. A Literature Survey of Benchmark Functions For Global Optimization Problems Int. Journal of Mathematical Modelling and Numerical Optimisation, 2013, 4, 150-194.


class XinSheYang04(dimensions=2)

Xin-She Yang 4 objective function.

This class defines the Xin-She Yang 4 global optimization problem. This is a multimodal minimization problem defined as follows:

f_{\text{XinSheYang04}}(x) = \left[ \sum_{i=1}^{n} \sin^2(x_i)
                             - e^{-\sum_{i=1}^{n} x_i^2} \right ]
                             e^{-\sum_{i=1}^{n} \sin^2 \sqrt{ \lvert
                             x_i \rvert }}

Here, n represents the number of dimensions and x_i \in [-10, 10] for i = 1, ..., n.

XinSheYang04 function

Two-dimensional XinSheYang04 function


Global optimum: f(x) = -1 for x_i = 0 for i = 1, ..., n

Jamil, M. & Yang, X.-S. A Literature Survey of Benchmark Functions For Global Optimization Problems Int. Journal of Mathematical Modelling and Numerical Optimisation, 2013, 4, 150-194.


class Xor(dimensions=9)

Xor objective function.

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

f_{\text{Xor}}(x) = \left[ 1 + \exp \left( - \frac{x_7}{1 + \exp(-x_1 - x_2 - x_5)} - \frac{x_8}{1 + \exp(-x_3 - x_4 - x_6)} - x_9 \right ) \right ]^{-2} \\
+ \left [ 1 + \exp \left( -\frac{x_7}{1 + \exp(-x_5)} - \frac{x_8}{1 + \exp(-x_6)} - x_9 \right ) \right] ^{-2} \\
+ \left [1 - \left\{1 + \exp \left(-\frac{x_7}{1 + \exp(-x_1 - x_5)} - \frac{x_8}{1 + \exp(-x_3 - x_6)} - x_9 \right ) \right\}^{-1} \right ]^2 \\
+ \left [1 - \left\{1 + \exp \left(-\frac{x_7}{1 + \exp(-x_2 - x_5)} - \frac{x_8}{1 + \exp(-x_4 - x_6)} - x_9 \right ) \right\}^{-1} \right ]^2

with x_i \in [-1, 1] for i=1,...,9.

Global optimum: f(x) = 0.9597588 for x = [1, -1, 1, -1, -1, 1, 1, -1, 0.421134]

Gavana, A. Global Optimization Benchmarks and AMPGO

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