class_docstr: Power sum objective function.
  
  This class defines the Power Sum global optimization problem. This
  is a multimodal minimization problem defined as follows:
  
  .. math::
  
      f_{\text{PowerSum}}(x) = \sum_{k=1}^n\left[\left(\sum_{i=1}^n x_i^k
      \right) - b_k \right]^2
  
  Where, in this exercise, :math:`b = [8, 18, 44, 114]`
  
  Here, :math:`x_i \in [0, 4]` for :math:`i = 1, ..., 4`.
  
  *Global optimum*: :math:`f(x) = 0` for :math:`x = [1, 2, 2, 3]`
  
  .. [1] Gavana, A. Global Optimization Benchmarks and AMPGO retrieved 2015
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code: def __init__(self, dimensions=4):
      Benchmark.__init__(self, dimensions)
  
      self._bounds = list(zip([0.0] * self.N, [4.0] * self.N))
  
      self.global_optimum = [[1.0, 2.0, 2.0, 3.0]]
      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 PowerSum object from inherited base classes: Benchmark with example: obj = PowerSum(dimensions)