class_docstr: Decanomial objective function.
  
  This class defines the Decanomial function global optimization problem. This
  is a multimodal minimization problem defined as follows:
  
  .. math::
  
     f_{\text{Decanomial}}(x) = 0.001 \left(\lvert{x_{2}^{4} + 12 x_{2}^{3}
     + 54 x_{2}^{2} + 108 x_{2} + 81.0}\rvert + \lvert{x_{1}^{10}
     - 20 x_{1}^{9} + 180 x_{1}^{8} - 960 x_{1}^{7} + 3360 x_{1}^{6}
     - 8064 x_{1}^{5} + 13340 x_{1}^{4} - 15360 x_{1}^{3} + 11520 x_{1}^{2}
     - 5120 x_{1} + 2624.0}\rvert\right)^{2}
  
  
  with :math:`x_i \in [-10, 10]` for :math:`i = 1, 2`.
  
  *Global optimum*: :math:`f(x) = 0` for :math:`x = [2, -3]`
  
  .. [1] Gavana, A. Global Optimization Benchmarks and AMPGO retrieved 2015
code: def __init__(self, dimensions=2):
      Benchmark.__init__(self, dimensions)
  
      self._bounds = list(zip([-10.0] * self.N, [10.0] * self.N))
      self.custom_bounds = [(0, 2.5), (-2, -4)]
  
      self.global_optimum = [[2.0, -3.0]]
      self.fglob = 0.0
  
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 Decanomial object from inherited base classes: Benchmark with example: obj = Decanomial(dimensions)