class_docstr: An EPO class, inherited from Optimizer.
  
  This is the designed class to define EPO-related
  variables and methods.
  
  References:
      G. Dhiman and V. Kumar. Emperor penguin optimizer: A bio-inspired algorithm for engineering problems.
      Knowledge-Based Systems (2018).
class_docstr_tok: ["An","EPO","class",",","inherited","from","Optimizer",".","This","is","the","designed","class","to","define","EPO-related","variables","and","methods",".","References",":","G.","Dhiman","and","V.","Kumar",".","Emperor","penguin","optimizer",":","A","bio-inspired","algorithm","for","engineering","problems",".","Knowledge-Based","Systems","(","2018",")","."]
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 -> EPO.")
  
      super(EPO, self).__init__()
  
      self.f = 2.0
      self.l = 1.5
  
      self.build(params)
  
      logger.info("Class overrided.")
  
file_path: opytimizer/opytimizer/optimizers/population/epo.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 EPO object from inherited base classes: Optimizer with example: obj = EPO(params)