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Python Implementation of CROSS-IN-TRAY FUNCTION

Updated: Aug 5, 2021






Mathematical Definition



Input Domain


The function is usually evaluated on the square xi ∈ [-10, 10], for all i = 1, 2.


Global Minima


The four global minima are located at

f(x0) = -2.06261, at x0 = (1.3491,-1.3491) , (1.3491,1.3491), (-1.3491,1.3491), and (-1.3491,-1.3491)


Description and Features


Dimension: 2


This class defines the Cross-in-Tray global optimization problem. This is a multimodal

minimization problem.


Cross-in-tray function are complex multi-peak function which tend to make the algorithm fall

into local optimization, so that the real optimum value cannot be obtained, which is used to test

the ability of the improved algorithm to deal with falling into premature.


Cross-in-tray function has multiple global minima. The function on a smaller domain depicts its

characteristics ‘cross’.


The function is continuous.

The function is non-convex.

The function is defined on 2-dimensional space.

The function is multimodal.

The function is non-differentiable.

The function is non-separable.



Python Implementation


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import matplotlib.pyplot as plt
from mpl_toolkits import mplot3d
from numpy import*

def f(x1,x2):
    a=fabs(100-sqrt(x1*x1+x2*x2)/pi)
    b=fabs(sin(x1)*sin(x2)*exp(a))+1
    c=-0.0001*b**0.1
    return c
x1=linspace(-10,10,100)
x2=linspace(-10,10,100)
X1,X2=meshgrid(x1,x2)

def plotter(E,A):
    fig=plt.figure(figsize=[12,8])
    ax=plt.axes(projection='3d')
    ax.plot_surface(X1,X2,f(X1,X2),color='yellow',alpha=0.7)
  
    ax.plot_wireframe(X1,X2,f(X1,X2), ccount=10,rcount=10,color='red, alpha=0.8)
    ax.view_init(elev=E,azim=A)
    ax.set_xlabel('x1')
    ax.set_ylabel('x2')
    ax.set_zlabel('f(x1,x2)')
    plt.show()

from ipywidgets import interactive
iplot=interactive(plotter,E=(-90,90,5),A=(-90,90,5))
iplot





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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