class_docstr: A GWO class, inherited from Optimizer.
  
  This is the designed class to define GWO-related
  variables and methods.
  
  References:
      S. Mirjalili, S. Mirjalili and A. Lewis. Grey Wolf Optimizer.
      Advances in Engineering Software (2014).
class_docstr_tok: ["A","GWO","class",",","inherited","from","Optimizer",".","This","is","the","designed","class","to","define","GWO-related","variables","and","methods",".","References",":","S.","Mirjalili",",","S.","Mirjalili","and","A.","Lewis",".","Grey","Wolf","Optimizer",".","Advances","in","Engineering","Software","(","2014",")","."]
docstr: Initialization method.
  
  Args:
      params: Contains key-value parameters to the meta-heuristics.
docstr_tok: ["Initialization","method",".","Args",":","params",":","Contains","key-value","parameters","to","the","meta-heuristics","."]
code: def __init__(self, params: Optional[Dict[str, Any]] = None) -> None:
      """Initialization method.
  
      Args:
          params: Contains key-value parameters to the meta-heuristics.
  
      """
  
      logger.info("Overriding class: Optimizer -> GWO.")
  
      super(GWO, self).__init__()
  
      self.build(params)
  
      logger.info("Class overrided.")
  
file_path: opytimizer/opytimizer/optimizers/population/gwo.py
imports: import copy
  from typing import Any, Dict, Optional, Tuple
  import numpy
  import opytimizer.math.random
  from opytimizer.core import Optimizer
  from opytimizer.core.function import Function
  from opytimizer.core.space import Space
  from opytimizer.utils import logging
label_desc: Use image node_id 1 to create a new GWO object from inherited base classes: Optimizer with example: obj = GWO(params)