class_docstr: Pathological objective function.
  
  This class defines the Pathological [1]_ global optimization problem. This is a
  multimodal minimization problem defined as follows:
  
  .. math::
  
      f_{\text{Pathological}}(x) = \sum_{i=1}^{n -1} \frac{\sin^{2}\left(
      \sqrt{100 x_{i+1}^{2} + x_{i}^{2}}\right) -0.5}{0.001 \left(x_{i}^{2}
      - 2x_{i}x_{i+1} + x_{i+1}^{2}\right)^{2} + 0.50}
  
  
  Here, :math:`n` represents the number of dimensions and
  :math:`x_i \in [-100, 100]` for :math:`i = 1, 2`.
  
  *Global optimum*: :math:`f(x) = 0.` for :math:`x = [0, 0]` for
  :math:`i = 1, 2`
  
  .. [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.
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code: def __init__(self, dimensions=2):
      Benchmark.__init__(self, dimensions)
  
      self._bounds = list(zip([-100.0] * self.N, [100.0] * self.N))
  
      self.global_optimum = [[0 for _ in range(self.N)]]
      self.fglob = 0.0
  
imports: from numpy import abs, sum, sin, cos, sqrt, log, prod, where, pi, exp, arange, floor, log10, atleast_2d, zeros
  from .go_benchmark import Benchmark
label_desc: Use image node_id 1 to create a new Pathological object from inherited base classes: Benchmark with example: obj = Pathological(dimensions)