Source code for niapy.benchmarks.powell
# encoding=utf8
"""Implementations of Powell function."""
from niapy.benchmarks.benchmark import Benchmark
__all__ = ['Powell']
[docs]class Powell(Benchmark):
r"""Implementations of Powell functions.
Date: 2018
Author: Klemen Berkovič
License: MIT
Function:
**Powell Function**
:math:`f(\textbf{x}) = \sum_{i = 1}^{D / 4} \left( (x_{4 i - 3} + 10 x_{4 i - 2})^2 + 5 (x_{4 i - 1} - x_{4 i})^2 + (x_{4 i - 2} - 2 x_{4 i - 1})^4 + 10 (x_{4 i - 3} - x_{4 i})^4 \right)`
**Input domain:**
The function can be defined on any input domain but it is usually
evaluated on the hypercube :math:`x_i ∈ [-4, 5]`, for all :math:`i = 1, 2,..., D`.
**Global minimum:**
:math:`f(\textbf{x}^*) = 0` at :math:`\textbf{x}^* = (0, \cdots, 0)`
LaTeX formats:
Inline:
$f(\textbf{x}) = \sum_{i = 1}^{D / 4} \left( (x_{4 i - 3} + 10 x_{4 i - 2})^2 + 5 (x_{4 i - 1} - x_{4 i})^2 + (x_{4 i - 2} - 2 x_{4 i - 1})^4 + 10 (x_{4 i - 3} - x_{4 i})^4 \right)$
Equation:
\begin{equation} f(\textbf{x}) = \sum_{i = 1}^{D / 4} \left( (x_{4 i - 3} + 10 x_{4 i - 2})^2 + 5 (x_{4 i - 1} - x_{4 i})^2 + (x_{4 i - 2} - 2 x_{4 i - 1})^4 + 10 (x_{4 i - 3} - x_{4 i})^4 \right) \end{equation}
Domain:
$-4 \leq x_i \leq 5$
Reference:
https://www.sfu.ca/~ssurjano/powell.html
"""
Name = ['Powell']
[docs] def __init__(self, lower=-4.0, upper=5.0):
r"""Initialize of Powell 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'''$f(\textbf{x}) = \sum_{i = 1}^{D / 4} \left( (x_{4 i - 3} + 10 x_{4 i - 2})^2 + 5 (x_{4 i - 1} - x_{4 i})^2 + (x_{4 i - 2} - 2 x_{4 i - 1})^4 + 10 (x_{4 i - 3} - x_{4 i})^4 \right)$'''
[docs] def function(self):
r"""Return benchmark evaluation function.
Returns:
Callable[[int, Union[int, float, List[int, float], numpy.ndarray]], float]: Fitness function.
"""
def f(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.
"""
v = 0.0
for i in range(1, (dimension // 4) + 1):
v += (x[4 * i - 4] + 10 * x[4 * i - 3]) ** 2 + 5 * (x[4 * i - 2] - x[4 * i - 1]) ** 2 + (x[4 * i - 3] - 2 * x[4 * i - 2]) ** 4 + 10 * (x[4 * i - 4] - x[4 * i - 1]) ** 4
return v
return f
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