Source code for niapy.problems.michalewicz

# encoding=utf8

"""Implementations of Michalewicz's function."""

import numpy as np
from niapy.problems.problem import Problem

__all__ = ['Michalewicz']


[docs]class Michalewicz(Problem): r"""Implementations of Michalewicz's functions. Date: 2018 Author: Klemen Berkovič License: MIT Function: **High Conditioned Elliptic Function** :math:`f(\textbf{x}) = \sum_{i=1}^D \left( 10^6 \right)^{ \frac{i - 1}{D - 1} } x_i^2` **Input domain:** The function can be defined on any input domain but it is usually evaluated on the hypercube :math:`x_i ∈ [0, \pi]`, for all :math:`i = 1, 2,..., D`. **Global minimum:** at :math:`d = 2` :math:`f(\textbf{x}^*) = -1.8013` at :math:`\textbf{x}^* = (2.20, 1.57)` at :math:`d = 5` :math:`f(\textbf{x}^*) = -4.687658` at :math:`d = 10` :math:`f(\textbf{x}^*) = -9.66015` LaTeX formats: Inline: $f(\textbf{x}) = - \sum_{i = 1}^{D} \sin(x_i) \sin\left( \frac{ix_i^2}{\pi} \right)^{2m}$ Equation: \begin{equation} f(\textbf{x}) = - \sum_{i = 1}^{D} \sin(x_i) \sin\left( \frac{ix_i^2}{\pi} \right)^{2m} \end{equation} Domain: $0 \leq x_i \leq \pi$ Reference URL: https://www.sfu.ca/~ssurjano/michal.html """
[docs] def __init__(self, dimension=4, lower=0.0, upper=np.pi, m=10, *args, **kwargs): r"""Initialize Michalewicz problem.. Args: dimension (Optional[int]): Dimension of the problem. lower (Optional[Union[float, Iterable[float]]]): Lower bounds of the problem. upper (Optional[Union[float, Iterable[float]]]): Upper bounds of the problem. m (float): Steepness of valleys and ridges. Recommended value is 10. See Also: :func:`niapy.problems.Problem.__init__` """ super().__init__(dimension, lower, upper, *args, **kwargs) self.m = m
[docs] @staticmethod def latex_code(): r"""Return the latex code of the problem. Returns: str: Latex code. """ return r'''$f(\textbf{x}) = - \sum_{i = 1}^{D} \sin(x_i) \sin\left( \frac{ix_i^2}{\pi} \right)^{2m}$'''
def _evaluate(self, x): return -np.sum(np.sin(x) * np.sin((np.arange(1, self.dimension + 1) * x ** 2.0) / np.pi) ** (2.0 * self.m))