class_docstr: A DOA class, inherited from Optimizer.
  
  This is the designed class to define DOA-related
  variables and methods.
  
  References:
      F. Demir et al. A survival classification method for hepatocellular carcinoma patients
      with chaotic Darcy optimization method based feature selection.
      Medical Hypotheses (2020).
class_docstr_tok: ["A","DOA","class",",","inherited","from","Optimizer",".","This","is","the","designed","class","to","define","DOA-related","variables","and","methods",".","References",":","F.","Demir","et","al",".","A","survival","classification","method","for","hepatocellular","carcinoma","patients","with","chaotic","Darcy","optimization","method","based","feature","selection",".","Medical","Hypotheses","(","2020",")","."]
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 -> DOA.")
  
      super(DOA, self).__init__()
  
      self.r = 1.0
  
      self.build(params)
  
      logger.info("Class overrided.")
  
file_path: opytimizer/opytimizer/optimizers/misc/doa.py
imports: from typing import Any, Dict, Optional
  import numpy
  import opytimizer.math.random
  import opytimizer.utils.exception
  from opytimizer.core import Optimizer
  from opytimizer.core.space import Space
  from opytimizer.utils import logging
label_desc: Use image node_id 1 to create a new DOA object from inherited base classes: Optimizer with example: obj = DOA(params)