class_docstr: Search of optimal performance on selected objective function
  over combinations of randomly selected hyperparameter values
  within specified bounds for specified number of selection iterations.
docstr: Initialize the Search
  
  Attributes
  ----------
  n_selection_iters: int
      number of iterations of random parameter selection
code: def __init__(
      self,
      optimizer,
      n_particles,
      dimensions,
      options,
      objective_func,
      iters,
      n_selection_iters,
      bounds=None,
      velocity_clamp=(0, 1),
  ):
      """Initialize the Search
  
      Attributes
      ----------
      n_selection_iters: int
          number of iterations of random parameter selection
      """
  
      # Assign n_selection_iters as attribute
      self.n_selection_iters = n_selection_iters
      # Assign attributes
      super(RandomSearch, self).__init__(
          optimizer,
          n_particles,
          dimensions,
          options,
          objective_func,
          iters,
          bounds=bounds,
          velocity_clamp=velocity_clamp,
      )
      # Invoke assertions
      self.assertions()
  
file_path: pyswarms/pyswarms/utils/search/random_search.py
imports: from __future__ import absolute_import, print_function, with_statement
  import numpy
  from past.builtins import xrange
  from pyswarms.utils.search.base_search import SearchBase
label_desc: Use image node_id 2 to create a new RandomSearch object from inherited base classes: SearchBase with example: obj = RandomSearch(optimizer, n_particles, dimensions, options, objective_func, iters, n_selection_iters, bounds, velocity_clamp)