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Jul 1, 20211 min

Shubert Function

Updated: Jul 18, 2021

Mathematical Definition

Input Domain

It can define into any input domain but usually its evaluated on the square 𝑥𝑖 ∈ [−10,10] for all i= 1,2

Global Minima

It has 18 global minima 𝑓(𝑥 ∗ ) ≈ −186.7309.

Description and Features

Shubert function is continuous function.

The function is differentiable.

The function is non-separable.

The function is defined on n – dimensional space.

Python Implementation

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import matplotlib.pyplot as plt
 
import numpy as np
 
from numpy import sin
 
from numpy import *
 
from numpy import pi
 
from numpy import sqrt
 
from matplotlib import cm
 

 
def f(x1,x2):
 
sum1=0
 
sum2=0
 
for i in range(1,6):
 
sum1 = sum1 + (i* cos(((i+1)*x1) +i))
 
sum2 = sum2 + (i* cos(((i+1)*x2) +i))
 
return sum1 * sum2
 

 
x1 =np.linspace(-10,10,100)
 
x2 =np.linspace(-10,10,100)
 
r_min,r_max= -10,10
 

 
x1,x2=np.meshgrid(x1,x2)
 
results=f(x1,x2)
 

 
figure=plt.figure(figsize=(9,9))
 
axis=figure.gca(projection='3d')
 
axis.contour3D(x1, x2, results,15)
 
axis.set_title('Shubert function')
 
axis.plot_surface(x1,x2,results, cmap=cm.rainbow)
 

 

 
axis.view_init(elev=21,azim=42)
 
axis.set_xlabel('X')
 
axis.set_ylabel('Y')
 
axis.set_zlabel('Z')
 
plt.contour(x1, x2, results,15)
 
plt.show()
 

 

References:

[1] Jamil, Momin, and Xin-She Yang. "A literature survey of benchmark functions for global optimization problems." International Journal of Mathematical Modelling and Numerical Optimization 4.2 (2013): 150-194.

#optimization #benchmarkfunction

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