class_docstr: Deckkers-Aarts objective function.
  
  This class defines the Deckkers-Aarts [1]_ global optimization problem. This
  is a multimodal minimization problem defined as follows:
  
  .. math::
  
      f_{\text{DeckkersAarts}}(x) = 10^5x_1^2 + x_2^2 - (x_1^2 + x_2^2)^2
      + 10^{-5}(x_1^2 + x_2^2)^4
  
  
  with :math:`x_i \in [-20, 20]` for :math:`i = 1, 2`.
  
  *Global optimum*: :math:`f(x) = -24776.518242168` for
  :math:`x = [0, \pm 14.9451209]`
  
  .. [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.
  
  TODO: jamil solution and global minimum are slightly wrong.
code: def __init__(self, dimensions=2):
      Benchmark.__init__(self, dimensions)
  
      self._bounds = list(zip([-20.0] * self.N, [20.0] * self.N))
      self.custom_bounds = ([-1, 1], [14, 16])
  
      self.global_optimum = [[0.0, 14.9451209]]
      self.fglob = -24776.518342168
  
imports: import numpy
  from numpy import abs, cos, exp, arange, pi, sin, sqrt, sum, zeros, tanh
  from numpy.testing import assert_almost_equal
  from .go_benchmark import Benchmark
label_desc: Use image node_id 1 to create a new DeckkersAarts object from inherited base classes: Benchmark with example: obj = DeckkersAarts(dimensions)