class_docstr: DeVilliers-Glasser 1 objective function.
  
  This class defines the DeVilliers-Glasser 1 [1]_ function global optimization
  problem. This is a multimodal minimization problem defined as follows:
  
  .. math::
  
     f_{\text{DeVilliersGlasser01}}(x) = \sum_{i=1}^{24} \left[ x_1x_2^{t_i}
     \sin(x_3t_i + x_4) - y_i \right ]^2
  
  
  Where, in this exercise, :math:`t_i = 0.1(i - 1)` and
  :math:`y_i = 60.137(1.371^{t_i}) \sin(3.112t_i + 1.761)`.
  
  Here, :math:`n` represents the number of dimensions and :math:`x_i \in
  [1, 100]` for :math:`i = 1, ..., 4`.
  
  *Global optimum*: :math:`f(x) = 0` for :math:`x_i = 0` for
  :math:`x = [60.137, 1.371, 3.112, 1.761]`.
  
  .. [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.
code: def fun(self, x, *args):
      self.nfev += 1
  
      t = 0.1 * arange(24)
      y = 60.137 * (1.371**t) * sin(3.112 * t + 1.761)
  
      return sum((x[0] * (x[1] ** t) * sin(x[2] * t + x[3]) - y) ** 2.0)
  
imports: import numpy
  from numpy import abs, cos, exp, arange, pi, sin, sqrt, sum, zeros, tanh
  from numpy.testing import assert_almost_equal
  from .go_benchmark import Benchmark
label_desc: Use image node_id 2 for calling the DeVilliersGlasser01 obj's underlying member method code with example usage: obj.fun(x) and returns: sum