Source code for niapy.benchmarks.michalewichz

# encoding=utf8

"""Implementations of Michalewichz's function."""

import numpy as np
from niapy.benchmarks.benchmark import Benchmark

__all__ = ['Michalewichz']


[docs]class Michalewichz(Benchmark): r"""Implementations of Michalewichz'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 """ Name = ['Michalewichz']
[docs] def __init__(self, lower=0.0, upper=np.pi, m=10): r"""Initialize of Michalewichz benchmark. Args: lower (Optional[float]): Lower bound of problem. upper (Optional[float]): Upper bound of problem. m (float): Steepness of valleys and ridges. Recommended value is 10. See Also: :func:`niapy.benchmarks.Benchmark.__init__` """ super().__init__(lower, upper) self.m = m
[docs] @staticmethod def latex_code(): r"""Return the latex code of the problem. Returns: str: Latex code. """ return r'''$f(\textbf{x}) = - \sum_{i = 1}^{D} \sin(x_i) \sin\left( \frac{ix_i^2}{\pi} \right)^{2m}$'''
[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. """ v = 0.0 for i in range(dimension): v += np.sin(x[i]) * np.sin(((i + 1) * x[i] ** 2) / np.pi) ** (2 * self.m) return -v return evaluate
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