Source code for NiaPy.benchmarks.qing

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

__all__ = ['Qing']


[docs]class Qing(object): r"""Implementation of Qing function. Date: 2018 Author: Lucija Brezočnik License: MIT Function: **Qing function** :math:`f(\mathbf{x}) = \sum_{i=1}^D \left(x_i^2 - i\right)^2` **Input domain:** The function can be defined on any input domain but it is usually evaluated on the hypercube :math:`x_i ∈ [-500, 500]`, for all :math:`i = 1, 2,..., D`. **Global minimum:** :math:`f(x^*) = 0`, at :math:`x^* = (\pm √i))` LaTeX formats: Inline: $f(\mathbf{x}) = \sum_{i=1}^D \left (x_i^2 - i\right)^2$ Equation: \begin{equation} f(\mathbf{x}) = \sum_{i=1}^D \left{(x_i^2 - i\right)}^2 \end{equation} Domain: $-500 \leq x_i \leq 500$ 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=-500.0, Upper=500.0): self.Lower = Lower self.Upper = Upper
[docs] @classmethod def function(cls): def evaluate(D, sol): val = 0.0 for i in range(D): val += math.pow(math.pow(sol[i], 2) - i, 2) return val
return evaluate