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Jul 4, 20211 min

Xin-She Yang N.3 Function

Mathematical Definition

Here m and β are the function parameters and usually taken as m=5 and β= 15.

Input Domain

Defined within the domain -2π < xi < 2π, for i = 1,2, ..., n. It can be defined on any input range as well but preferred to be as given above.

Global Minima

It has many local minima and the unique global minimum f(x*) = -1 at x* = (0,0,..., 0) for m=5 and β = 15.

Description and Features

  • Uni-model function.

  • Differentiable.

  • Convex.

  • Non-separable

  • Smooth

Python Implementation

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import matplotlib.pyplot as plt
 
import matplotlib as mpl
 
from mpl_toolkits import mplot3d
 
from mpl_toolkits.mplot3d import Axes3D
 
import numpy as np
 
from numpy import*
 

 
%matplotlib notebook
 
plt.rcParams['figure.figsize'] = (6,4)
 
plt.rcParams['figure.dpi']=150
 
fig=plt.figure()
 
ax=fig.add_subplot(111,projection='3d')
 

 
ax= plt.axes(projection='3d')
 

 
def f(x1,x2):
 
a = exp( -(x1/15)**10)-2 * exp ( (-x1**2) (x2**2)) * cos(x1) * cos(x1) * cos(x2) * cos(x2)
 
return a
 

 
x1= linspace(-2*pi,2*pi)
 
x2= linspace(-2*pi,2*pi)
 
X1,X2= meshgrid(x1,x2)
 
ax.plot_surface(X1,X2,f(X1,X2), cmap='jet')
 
ax.set_xlabel('x1')
 
ax.set_ylabel('x2')
 
ax.set_zlabel('f(x1,x2)')
 
ax.view_init(10,10)
 
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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