Source code for niapy.benchmarks.whitley
# encoding=utf8
"""Whitley function."""
import math
from niapy.benchmarks.benchmark import Benchmark
__all__ = ['Whitley']
[docs]class Whitley(Benchmark):
r"""Implementation of Whitley function.
Date: 2018
Authors: Grega Vrbančič and Lucija Brezočnik
License: MIT
Function: **Whitley function**
:math:`f(\mathbf{x}) = \sum_{i=1}^D \sum_{j=1}^D
\left(\frac{(100(x_i^2-x_j)^2 + (1-x_j)^2)^2}{4000} -
\cos(100(x_i^2-x_j)^2 + (1-x_j)^2)+1\right)`
**Input domain:**
The function can be defined on any input domain but it is usually
evaluated on the hypercube :math:`x_i ∈ [-10.24, 10.24]`, for all :math:`i = 1, 2,..., D`.
**Global minimum:** :math:`f(x^*) = 0`, at :math:`x^* = (1,...,1)`
LaTeX formats:
Inline:
$f(\mathbf{x}) =
\sum_{i=1}^D \sum_{j=1}^D \left(\frac{(100(x_i^2-x_j)^2 +
(1-x_j)^2)^2}{4000} - \cos(100(x_i^2-x_j)^2 + (1-x_j)^2)+1\right)$
Equation:
\begin{equation}f(\mathbf{x}) =
\sum_{i=1}^D \sum_{j=1}^D \left(\frac{(100(x_i^2-x_j)^2 +
(1-x_j)^2)^2}{4000} - \cos(100(x_i^2-x_j)^2 +
(1-x_j)^2)+1\right) \end{equation}
Domain:
$-10.24 \leq x_i \leq 10.24$
Reference paper:
Jamil, M., and Yang, X. S. (2013).
A literature survey of benchmark functions for global optimisation problems.
International Journal of Mathematical Modelling and Numerical Optimisation,
4(2), 150-194.
"""
Name = ['Whitley']
[docs] def __init__(self, lower=-10.24, upper=10.24):
r"""Initialize of Whitley benchmark.
Args:
lower (Optional[float]): Lower bound of problem.
upper (Optional[float]): Upper bound of problem.
See Also:
:func:`niapy.benchmarks.Benchmark.__init__`
"""
super().__init__(lower, upper)
[docs] @staticmethod
def latex_code():
r"""Return the latex code of the problem.
Returns:
str: Latex code.
"""
return r'''$f(\mathbf{x}) =
\sum_{i=1}^D \sum_{j=1}^D \left(\frac{(100(x_i^2-x_j)^2 +
(1-x_j)^2)^2}{4000} - \cos(100(x_i^2-x_j)^2 + (1-x_j)^2)+1\right)$'''
[docs] def function(self):
r"""Return benchmark evaluation function.
Returns:
Callable[[int, Union[int, float, List[int, float], numpy.ndarray]], float]: Fitness function.
"""
def evaluate(dimension, x):
r"""Fitness function.
Args:
dimension (int): Dimensionality of the problem
x (Union[int, float, List[int, float], numpy.ndarray]): Solution to check.
Returns:
float: Fitness value for the solution.
"""
val = 0.0
for i in range(dimension):
for j in range(dimension):
temp = 100 * math.pow((math.pow(x[i], 2) - x[j]), 2) + math.pow(1 - x[j], 2)
val += (float(math.pow(temp, 2)) / 4000.0) - math.cos(temp) + 1
return val
return evaluate