class_docstr: Yao-Liu 4 objective function.
  
  This class defines the Yao-Liu function 4 [1]_ global optimization problem. This
  is a multimodal minimization problem defined as follows:
  
  .. math::
  
      f_{\text{YaoLiu04}}(x) = {max}_i \left\{ \left | x_i \right | ,
                               1 \leq i \leq n \right\}
  
  Here, :math:`n` represents the number of dimensions and
  :math:`x_i \in [-10, 10]` for :math:`i = 1, ..., n`.
  
  *Global optimum*: :math:`f(x) = 0` for :math:`x_i = 0` for
  :math:`i = 1, ..., n`
  
  .. [1] Yao X., Liu Y. (1997) Fast evolution strategies.
  In: Angeline P.J., Reynolds R.G., McDonnell J.R., Eberhart R. (eds)
  Evolutionary Programming VI. EP 1997.
  Lecture Notes in Computer Science, vol 1213. Springer, Berlin, Heidelberg
  
  .. [2] Mishra, S. Global Optimization by Differential Evolution and
  Particle Swarm Methods: Evaluation on Some Benchmark Functions.
  Munich Personal RePEc Archive, 2006, 1005
  
  TODO line 1201.  Gavana code and documentation differ.
  max(abs(x)) != abs(max(x))
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 = [[0 for _ in range(self.N)]]
      self.fglob = 0.0
      self.change_dimensionality = True
  
file_path: scipy/benchmarks/benchmarks/go_benchmark_functions/go_funcs_Y.py
imports: from numpy import abs, sum, cos, pi
  from .go_benchmark import Benchmark
label_desc: Use image node_id 1 to create a new YaoLiu04 object from inherited base classes: Benchmark with example: obj = YaoLiu04(dimensions)