class_docstr: An STOA class, inherited from Optimizer.
  
  This is the designed class to define STOA-related
  variables and methods.
  
  References:
      G. Dhiman and A. Kaur. STOA: A bio-inspired based optimization algorithm for industrial engineering problems.
      Engineering Applications of Artificial Intelligence (2019).
class_docstr_tok: ["An","STOA","class",",","inherited","from","Optimizer",".","This","is","the","designed","class","to","define","STOA-related","variables","and","methods",".","References",":","G.","Dhiman","and","A.","Kaur",".","STOA",":","A","bio-inspired","based","optimization","algorithm","for","industrial","engineering","problems",".","Engineering","Applications","of","Artificial","Intelligence","(","2019",")","."]
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 -> STOA.")
  
      super(STOA, self).__init__()
  
      self.Cf = 2.0
      self.u = 1.0
      self.v = 1.0
  
      self.build(params)
  
      logger.info("Class overrided.")
  
file_path: opytimizer/opytimizer/optimizers/swarm/stoa.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 STOA object from inherited base classes: Optimizer with example: obj = STOA(params)