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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