Source code for NiaPy.benchmarks.sumSquares

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

__all__ = ['SumSquares']


[docs]class SumSquares(object): r"""Implementation of Sum Squares functions. Date: 2018 Authors: Lucija Brezočnik License: MIT Function: **Sum Squares function** :math:`f(\mathbf{x}) = \sum_{i=1}^D i 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 ∈ [-10, 10]`, for all :math:`i = 1, 2,..., D`. **Global minimum:** :math:`f(x^*) = 0`, at :math:`x^* = (0,...,0)` LaTeX formats: Inline: $f(\mathbf{x}) = \sum_{i=1}^D i x_i^2$ Equation: \begin{equation}f(\mathbf{x}) = \sum_{i=1}^D i x_i^2 \end{equation} Domain: $0 \leq x_i \leq 10$ 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=-10.0, Upper=10.0): self.Lower = Lower self.Upper = Upper
[docs] @classmethod def function(cls): def evaluate(D, sol): val = 0.0 for i in range(D): val += i * math.pow(sol[i], 2) return val
return evaluate