class_docstr: Price 1 objective function.
  
  This class defines the Price 1 [1]_ global optimization problem. This
  is a multimodal minimization problem defined as follows:
  
  .. math::
  
      f_{\text{Price01}}(x) = (\lvert x_1 \rvert - 5)^2
      + (\lvert x_2 \rvert - 5)^2
  
  
  with :math:`x_i \in [-500, 500]` for :math:`i = 1, 2`.
  
  *Global optimum*: :math:`f(x_i) = 0.0` for :math:`x = [5, 5]` or
  :math:`x = [5, -5]` or :math:`x = [-5, 5]` or :math:`x = [-5, -5]`.
  
  .. [1] Price, W. A controlled random search procedure for global
  optimisation Computer Journal, 1977, 20, 367-370
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code: def __init__(self, dimensions=2):
      Benchmark.__init__(self, dimensions)
  
      self._bounds = list(zip([-500.0] * self.N, [500.0] * self.N))
      self.custom_bounds = ([-10.0, 10.0], [-10.0, 10.0])
  
      self.global_optimum = [[5.0, 5.0]]
      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 Price01 object from inherited base classes: Benchmark with example: obj = Price01(dimensions)