class_docstr: PenHolder objective function.
  
  This class defines the PenHolder [1]_ global optimization problem. This is a
  multimodal minimization problem defined as follows:
  
  .. math::
  
      f_{\text{PenHolder}}(x) = -e^{\left|{e^{-\left|{- \frac{\sqrt{x_{1}^{2}
      + x_{2}^{2}}}{\pi} + 1}\right|} \cos\left(x_{1}\right)
      \cos\left(x_{2}\right)}\right|^{-1}}
  
  
  with :math:`x_i \in [-11, 11]` for :math:`i = 1, 2`.
  
  *Global optimum*: :math:`f(x_i) = -0.9635348327265058` for
  :math:`x_i = \pm 9.646167671043401` 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([-11.0] * self.N, [11.0] * self.N))
  
      self.global_optimum = [[-9.646167708023526, 9.646167671043401]]
      self.fglob = -0.9635348327265058
  
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 PenHolder object from inherited base classes: Benchmark with example: obj = PenHolder(dimensions)