Schaffer N. 4 Function
Mathematical Definition
\[f(x, y)=0.5 + \frac{cos^2(sin(|x^2-y^2|))-0.5}{(1+0.001(x^2+y^2))^2}\]Plots
Two contours of the function are as presented below:
Description and Features
- The function is continuous.
- The function is not convex.
- The function is defined on 2-dimensional space.
- The function is unimodal.
- The function is differentiable.
- The function is not separable.
Input Domain
The function can be defined on any input domain but it is usually evaluated on $x_i \in [-100, 100]$ for $i=1, 2$.
Global Minima
The function has one global minimum $f(\textbf{x}^{\ast})=0.292579$ at $\textbf{x}^{\ast} = (0, 1.253115)$.
Implementation
Python
For Python, the function is implemented in the benchmarkfcns package, which can be installed from command line with pip install benchmarkfcns
.
from benchmarkfcns import schaffer4
print(schaffer4([[0, 0],
[1, 1]]))
MATLAB
An implementation of the Schaffer N. 4 Function with MATLAB is provided below.
% Computes the value of the Schaffer N. 4 function.
% SCORES = SCHAFFERN4FCN(X) computes the value of the Schaffer N. 4
% function at point X. SCHAFFERN4FCN accepts a matrix of size M-by-2 and
% returns a vetor SCORES of size M-by-1 in which each row contains the
% function value for the corresponding row of X.
% For more information please visit:
% https://en.wikipedia.org/wiki/Test_functions_for_optimization
%
% Author: Mazhar Ansari Ardeh
% Please forward any comments or bug reports to mazhar.ansari.ardeh at
% Google's e-mail service or feel free to kindly modify the repository.
function scores = schaffern4fcn(x)
n = size(x, 2);
assert(n == 2, 'Schaffer function N. 4 is only defined on a 2D space.')
X = x(:, 1);
Y = x(:, 2);
numeratorcomp = (cos(sin(abs(X .^ 2 - Y .^ 2))) .^ 2) - 0.5;
denominatorcomp = (1 + 0.001 * (X .^2 + Y .^2)) .^2 ;
scores = 0.5 + numeratorcomp ./ denominatorcomp;
end
The function can be represented in Latex as follows:
f(x, y)=0.5 + \frac{cos^2(sin(|x^2-y^2|))-0.5}{(1+0.001(x^2+y^2))^2}
References:
- https://en.wikipedia.org/wiki/Test_functions_for_optimization
- rpackages.ianhowson.com/cran/smoof/man/makeSchafferN4Function.html
- Momin Jamil and Xin-She Yang, A literature survey of benchmark functions for global optimization problems, Int. Journal of Mathematical Modelling and Numerical Optimisation}, Vol. 4, No. 2, pp. 150–194 (2013), arXiv:1308.4008
- S. K. Mishra, “Some New Test Functions For Global Optimization And Performance of Repulsive Particle Swarm Method,” [Available Online]: http://mpra.ub.uni-muenchen.de/2718/