class_docstr: Paviani objective function.
  
  This class defines the Paviani [1]_ global optimization problem. This is a
  multimodal minimization problem defined as follows:
  
  .. math::
  
      f_{\text{Paviani}}(x) = \sum_{i=1}^{10} \left[\log^{2}\left(10
      - x_i\right) + \log^{2}\left(x_i -2\right)\right]
      - \left(\prod_{i=1}^{10} x_i^{10} \right)^{0.2}
  
  
  with :math:`x_i \in [2.001, 9.999]` for :math:`i = 1, ... , 10`.
  
  *Global optimum*: :math:`f(x_i) = -45.7784684040686` for
  :math:`x_i = 9.350266` for :math:`i = 1, ..., 10`
  
  .. [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: think Gavana web/code definition is wrong because final product term
  shouldn't raise x to power 10.
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code: def __init__(self, dimensions=10):
      Benchmark.__init__(self, dimensions)
  
      self._bounds = list(zip([2.001] * self.N, [9.999] * self.N))
  
      self.global_optimum = [[9.350266 for _ in range(self.N)]]
      self.fglob = -45.7784684040686
  
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 Paviani object from inherited base classes: Benchmark with example: obj = Paviani(dimensions)