class_docstr: Colville objective function.
  
  This class defines the Colville global optimization problem. This
  is a multimodal minimization problem defined as follows:
  
  .. math::
  
      f_{\text{Colville}}(x) = \left(x_{1} -1\right)^{2}
      + 100 \left(x_{1}^{2} - x_{2}\right)^{2}
      + 10.1 \left(x_{2} -1\right)^{2} + \left(x_{3} -1\right)^{2}
      + 90 \left(x_{3}^{2} - x_{4}\right)^{2}
      + 10.1 \left(x_{4} -1\right)^{2} + 19.8 \frac{x_{4} -1}{x_{2}}
  
  
  with :math:`x_i \in [-10, 10]` for :math:`i = 1, ..., 4`.
  
  *Global optimum*: :math:`f(x) = 0` for :math:`x_i = 1` for
  :math:`i = 1, ..., 4`
  
  .. [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 docstring equation is wrong use Jamil#36
code: def __init__(self, dimensions=4):
      Benchmark.__init__(self, dimensions)
  
      self._bounds = list(zip([-10.0] * self.N, [10.0] * self.N))
  
      self.global_optimum = [[1 for _ in range(self.N)]]
      self.fglob = 0.0
  
imports: import numpy
  from numpy import abs, asarray, cos, exp, floor, pi, sign, sin, sqrt, sum, size, tril, isnan, atleast_2d, repeat
  from numpy.testing import assert_almost_equal
  from .go_benchmark import Benchmark
label_desc: Use image node_id 1 to create a new Colville object from inherited base classes: Benchmark with example: obj = Colville(dimensions)