class_docstr: PermFunction 1 objective function.
  
  This class defines the PermFunction1 [1]_ global optimization problem. This is
  a multimodal minimization problem defined as follows:
  
  .. math::
  
      f_{\text{PermFunction01}}(x) = \sum_{k=1}^n \left\{ \sum_{j=1}^n (j^k
      + \beta) \left[ \left(\frac{x_j}{j}\right)^k - 1 \right] \right\}^2
  
  
  Here, :math:`n` represents the number of dimensions and
  :math:`x_i \in [-n, n + 1]` for :math:`i = 1, ..., n`.
  
  *Global optimum*: :math:`f(x) = 0` for :math:`x_i = i` for
  :math:`i = 1, ..., n`
  
  .. [1] Mishra, S. Global Optimization by Differential Evolution and
  Particle Swarm Methods: Evaluation on Some Benchmark Functions.
  Munich Personal RePEc Archive, 2006, 1005
  
  TODO: line 560
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code: def __init__(self, dimensions=2):
      Benchmark.__init__(self, dimensions)
  
      self._bounds = list(
          zip([-self.N] * self.N, [self.N + 1] * self.N)
      )
  
      self.global_optimum = [list(range(1, self.N + 1))]
      self.fglob = 0.0
      self.change_dimensionality = True
  
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 PermFunction01 object from inherited base classes: Benchmark with example: obj = PermFunction01(dimensions)