Source code for NiaPy.benchmarks.griewank

# encoding=utf8
# pylint: disable=anomalous-backslash-in-string
import math

__all__ = ['Griewank']


[docs]class Griewank(object): r"""Implementation of Griewank function. Date: 2018 Authors: Iztok Fister Jr. and Lucija Brezočnik License: MIT Function: **Griewank function** :math:`f(\mathbf{x}) = \sum_{i=1}^D \frac{x_i^2}{4000} - \prod_{i=1}^D \cos(\frac{x_i}{\sqrt{i}}) + 1` **Input domain:** The function can be defined on any input domain but it is usually evaluated on the hypercube :math:`x_i ∈ [-100, 100]`, for all :math:`i = 1, 2,..., D`. **Global minimum:** :math:`f(x^*) = 0`, at :math:`x^* = (0,...,0)` LaTeX formats: Inline: $f(\mathbf{x}) = \sum_{i=1}^D \frac{x_i^2}{4000} - \prod_{i=1}^D \cos(\frac{x_i}{\sqrt{i}}) + 1$ Equation: \begin{equation} f(\mathbf{x}) = \sum_{i=1}^D \frac{x_i^2}{4000} - \prod_{i=1}^D \cos(\frac{x_i}{\sqrt{i}}) + 1 \end{equation} Domain: $-100 \leq x_i \leq 100$ Reference paper: Jamil, M., and Yang, X. S. (2013). A literature survey of benchmark functions for global optimisation problems. International Journal of Mathematical Modelling and Numerical Optimisation, 4(2), 150-194. """ def __init__(self, Lower=-100.0, Upper=100.0): self.Lower = Lower self.Upper = Upper
[docs] @classmethod def function(cls): def evaluate(D, sol): val1 = 0.0 val2 = 1.0 for i in range(D): val1 += (math.pow(sol[i], 2) / 4000.0) val2 *= (math.cos(sol[i] / math.sqrt(i + 1))) return val1 - val2 + 1.0
return evaluate