class_docstr: A PIO class, inherited from Optimizer.
  
  This is the designed class to define PIO-related
  variables and methods.
  
  References:
      H. Duan and P. Qiao.
      Pigeon-inspired optimization:a new swarm intelligence optimizerfor air robot path planning.
      International Journal of IntelligentComputing and Cybernetics (2014).
class_docstr_tok: ["A","PIO","class",",","inherited","from","Optimizer",".","This","is","the","designed","class","to","define","PIO-related","variables","and","methods",".","References",":","H.","Duan","and","P.","Qiao",".","Pigeon-inspired","optimization",":","a","new","swarm","intelligence","optimizerfor","air","robot","path","planning",".","International","Journal","of","IntelligentComputing","and","Cybernetics","(","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 -> PIO.")
  
      super(PIO, self).__init__()
  
      self.n_c1 = 150
      self.n_c2 = 200
  
      self.R = 0.2
  
      self.build(params)
  
      logger.info("Class overrided.")
  
imports: from typing import Any, Dict, List, Optional
  import numpy
  import opytimizer.math.random
  import opytimizer.utils.constant
  import opytimizer.utils.exception
  from opytimizer.core import Optimizer
  from opytimizer.core.agent import Agent
  from opytimizer.core.space import Space
  from opytimizer.utils import logging
label_desc: Use image node_id 1 to create a new PIO object from inherited base classes: Optimizer with example: obj = PIO(params)