Source code for niapy.benchmarks.levy
# encoding=utf8
"""Implementations of Levy function."""
import numpy as np
from niapy.benchmarks.benchmark import Benchmark
__all__ = ['Levy']
[docs]class Levy(Benchmark):
r"""Implementations of Levy functions.
Date: 2018
Author: Klemen Berkovič
License: MIT
Function:
**Levy Function**
:math:`f(\textbf{x}) = \sin^2 (\pi w_1) + \sum_{i = 1}^{D - 1} (w_i - 1)^2 \left( 1 + 10 \sin^2 (\pi w_i + 1) \right) + (w_d - 1)^2 (1 + \sin^2 (2 \pi w_d)) \\ w_i = 1 + \frac{x_i - 1}{4}`
**Input domain:**
The function can be defined on any input domain but it is usually
evaluated on the hypercube :math:`x_i ∈ [-10, 10]`, for all :math:`i = 1, 2,..., D`.
**Global minimum:**
:math:`f(\textbf{x}^*) = 0` at :math:`\textbf{x}^* = (1, \cdots, 1)`
LaTeX formats:
Inline:
$f(\textbf{x}) = \sin^2 (\pi w_1) + \sum_{i = 1}^{D - 1} (w_i - 1)^2 \left( 1 + 10 \sin^2 (\pi w_i + 1) \right) + (w_d - 1)^2 (1 + \sin^2 (2 \pi w_d)) \\ w_i = 1 + \frac{x_i - 1}{4}$
Equation:
\begin{equation} f(\textbf{x}) = \sin^2 (\pi w_1) + \sum_{i = 1}^{D - 1} (w_i - 1)^2 \left( 1 + 10 \sin^2 (\pi w_i + 1) \right) + (w_d - 1)^2 (1 + \sin^2 (2 \pi w_d)) \\ w_i = 1 + \frac{x_i - 1}{4} \end{equation}
Domain:
$-10 \leq x_i \leq 10$
Reference:
https://www.sfu.ca/~ssurjano/levy.html
"""
Name = ['Levy']
[docs] def __init__(self, lower=0.0, upper=np.pi):
r"""Initialize of Levy 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'''$f(\textbf{x}) = \sin^2 (\pi w_1) + \sum_{i = 1}^{D - 1} (w_i - 1)^2 \left( 1 + 10 \sin^2 (\pi w_i + 1) \right) + (w_d - 1)^2 (1 + \sin^2 (2 \pi w_d)) \\ w_i = 1 + \frac{x_i - 1}{4}$'''
[docs] def function(self):
r"""Return benchmark evaluation function.
Returns:
Callable[[int, Union[int, float, List[int, float], numpy.ndarray]], float]: Fitness function.
"""
def w(x):
return 1 + (x - 1) / 4
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(dimension - 1):
v += (w(x[i]) - 1) ** 2 * (1 + 10 * np.sin(np.pi * w(x[i]) + 1) ** 2) + (w(x[-1]) - 1) ** 2 * (1 + np.sin(2 * np.pi * w(x[-1]) ** 2))
return np.sin(np.pi * w(x[0])) ** 2 + v
return f
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