class_docstr: A WAOA class, inherited from Optimizer.
  
  This is the designed class to define WAOA-related
  variables and methods.
  
  References:
      P. Trojovský and M. Dehghani. A new bio-inspired metaheuristic algorithm for
      solving optimization problems based on walruses behavior. Scientific Reports (2023).
docstr: Evaluates the search space according to the objective function.
  
  Args:
      space: A Space object that will be evaluated.
docstr_tok: ["Evaluates","the","search","space","according","to","the","objective","function",".","Args",":","space",":","A","Space","object","that","will","be","evaluated","."]
code: def evaluate(self, space: Space) -> None:
      """Evaluates the search space according to the objective function.
  
      Args:
          space: A Space object that will be evaluated.
  
      """
      print("evaluating...")
      for agent in space.agents:
          if agent.fit < space.best_agent.fit:
              space.best_agent.position = copy.deepcopy(agent.position)
              space.best_agent.fit = copy.deepcopy(agent.fit)
              space.best_agent.ts = int(time.time())
  
file_path: opytimizer/opytimizer/optimizers/swarm/waoa.py
imports: import copy
  import time
  from typing import Any, Dict, Optional
  import numpy
  import opytimizer.math.random
  import opytimizer.utils.logging
  from opytimizer.core.function import Function
  from opytimizer.core.optimizer import Optimizer
  from opytimizer.core.space import Space
label_desc: Use image node_id 2 for calling the WAOA obj's underlying member method code with example usage: obj.evaluate(space) without return types