class_docstr: Mishra 3 objective function.
  
  This class defines the Mishra 3 [1]_ global optimization problem. This
  is a multimodal minimization problem defined as follows:
  
  .. math::
  
     f_{\text{Mishra03}}(x) = \sqrt{\lvert \cos{\sqrt{\lvert x_1^2
     + x_2^2 \rvert}} \rvert} + 0.01(x_1 + x_2)
  
  
  with :math:`x_i \in [-10, 10]` for :math:`i = 1, 2`.
  
  *Global optimum*: :math:`f(x) = -0.1999` for
  :math:`x = [-9.99378322, -9.99918927]`
  
  .. [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: I think that Jamil#76 has the wrong global minimum, a smaller one
  is possible
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code: def __init__(self, dimensions=2):
      Benchmark.__init__(self, dimensions)
  
      self._bounds = list(zip([-10.0] * self.N, [10.0] * self.N))
  
      self.global_optimum = [[-9.99378322, -9.99918927]]
      self.fglob = -0.19990562
  
imports: from numpy import abs, asarray, cos, exp, log, arange, pi, prod, sin, sqrt, sum, tan
  from .go_benchmark import Benchmark, safe_import
label_desc: Use image node_id 1 to create a new Mishra03 object from inherited base classes: Benchmark with example: obj = Mishra03(dimensions)