class_docstr: Langermann objective function.
  
  This class defines the Langermann [1]_ global optimization problem. This
  is a multimodal minimization problem defined as follows:
  
  .. math::
  
      f_{\text{Langermann}}(x) = - \sum_{i=1}^{5} 
      \frac{c_i \cos\left\{\pi \left[\left(x_{1}- a_i\right)^{2}
      + \left(x_{2} - b_i \right)^{2}\right]\right\}}{e^{\frac{\left( x_{1}
      - a_i\right)^{2} + \left( x_{2} - b_i\right)^{2}}{\pi}}}
  
  
  Where:
  
  .. math::
  
      \begin{matrix}
      a = [3, 5, 2, 1, 7]\\
      b = [5, 2, 1, 4, 9]\\
      c = [1, 2, 5, 2, 3] \\
      \end{matrix}
  
  
  Here :math:`x_i \in [0, 10]` for :math:`i = 1, 2`.
  
  *Global optimum*: :math:`f(x) = -5.1621259`
  for :math:`x = [2.00299219, 1.006096]`
  
  .. [1] Gavana, A. Global Optimization Benchmarks and AMPGO retrieved 2015
  
  TODO: Langermann from Gavana is _not the same_ as Jamil #68.
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code: def __init__(self, dimensions=2):
      Benchmark.__init__(self, dimensions)
  
      self._bounds = list(zip([0.0] * self.N, [10.0] * self.N))
  
      self.global_optimum = [[2.00299219, 1.006096]]
      self.fglob = -5.1621259
  
imports: from numpy import sum, cos, exp, pi, arange, sin
  from .go_benchmark import Benchmark
label_desc: Use image node_id 1 to create a new Langermann object from inherited base classes: Benchmark with example: obj = Langermann(dimensions)