class_docstr: An AO class, inherited from Optimizer.
  
  This is the designed class to define AO-related
  variables and methods.
  
  References:
      L. Abualigah et al. Aquila Optimizer: A novel meta-heuristic optimization Algorithm.
      Computers & Industrial Engineering (2021).
class_docstr_tok: ["An","AO","class",",","inherited","from","Optimizer",".","This","is","the","designed","class","to","define","AO-related","variables","and","methods",".","References",":","L.","Abualigah","et","al",".","Aquila","Optimizer",":","A","novel","meta-heuristic","optimization","Algorithm",".","Computers","&","Industrial","Engineering","(","2021",")","."]
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 -> AO.")
  
      super(AO, self).__init__()
  
      self.alpha = 0.1
      self.delta = 0.1
  
      self.n_cycles = 10
  
      self.U = 0.00565
      self.w = 0.005
  
      self.build(params)
  
      logger.info("Class overrided.")
  
imports: import copy
  from typing import Any, Dict, Optional
  import numpy
  import opytimizer.math.distribution
  import opytimizer.math.random
  import opytimizer.utils.exception
  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 AO object from inherited base classes: Optimizer with example: obj = AO(params)