class_docstr: Griewank objective function.
  
  This class defines the Griewank global optimization problem. This
  is a multimodal minimization problem defined as follows:
  
  .. math::
  
      f_{\text{Griewank}}(x) = \frac{1}{4000}\sum_{i=1}^n x_i^2
      - \prod_{i=1}^n\cos\left(\frac{x_i}{\sqrt{i}}\right) + 1
  
  
  Here, :math:`n` represents the number of dimensions and
  :math:`x_i \in [-600, 600]` for :math:`i = 1, ..., n`.
  
  *Global optimum*: :math:`f(x) = 0` for :math:`x_i = 0` for
  :math:`i = 1, ..., n`
  
  .. [1] Jamil, M. & Yang, X.-S. A Literature Survey of Benchmark Functions
  For Global Optimization Problems Int. Journal of Mathematical Modelling
  and Numerical Optimisation, 2013, 4, 150-194.
code: def __init__(self, dimensions=2):
      Benchmark.__init__(self, dimensions)
  
      self._bounds = list(zip([-100.0] * self.N, [100.0] * self.N))
      self.custom_bounds = [(-50, 50), (-50, 50)]
  
      self.global_optimum = [[0 for _ in range(self.N)]]
      self.fglob = 0.0
      self.change_dimensionality = True
  
imports: import numpy
  from numpy import abs, sin, cos, exp, floor, log, arange, prod, sqrt, sum
  from .go_benchmark import Benchmark
label_desc: Use image node_id 1 to create a new Griewank object from inherited base classes: Benchmark with example: obj = Griewank(dimensions)