class_docstr: A SBO class, inherited from Optimizer.
  
  This is the designed class to define SBO-related
  variables and methods.
  
  References:
      S. H. S. Moosavi and V. K. Bardsiri.
      Satin bowerbird optimizer: a new optimization algorithm to optimize ANFIS
      for software development effort estimation.
      Engineering Applications of Artificial Intelligence (2017).
docstr: Wraps Satin Bowerbird Optimizer over all agents and variables (eq. 1-7).
  
  Args:
      space: Space containing agents and update-related information.
      function: A Function object that will be used as the objective function.
docstr_tok: ["Wraps","Satin","Bowerbird","Optimizer","over","all","agents","and","variables","(","eq",".","1-7",")",".","Args",":","space",":","Space","containing","agents","and","update-related","information",".","function",":","A","Function","object","that","will","be","used","as","the","objective","function","."]
code: def update(self, space: Space, function: Function) -> None:
      """Wraps Satin Bowerbird Optimizer over all agents and variables (eq. 1-7).
  
      Args:
          space: Space containing agents and update-related information.
          function: A Function object that will be used as the objective function.
  
      """
  
      fitness = [
          1 / (1 + agent.fit)
          if agent.fit >= 0
          else 1 + np.abs(agent.fit)
          for agent in space.agents
      ]
      total_fitness = np.sum(fitness)
      probs = [fit / total_fitness for fit in fitness]
  
      for agent in space.agents:
          for j in range(agent.n_variables):
              s = d.generate_choice_distribution(
                  len(space.agents), probs, 1
              )[0]
  
              lambda_k = self.alpha / (1 + probs[s])
  
              agent.position[j] += lambda_k * (
                  (
                      space.agents[s].position[j]
                      + space.best_agent.position[j]
                  )
                  / 2
                  - agent.position[j]
              )
  
              r1 = r.generate_uniform_random_number()
              if r1 < self.p_mutation:
                  agent.position[j] += (
                      self.sigma[j]
                      * r.generate_gaussian_random_number()
                  )
          agent.clip_by_bound()
  
          agent.fit = function(agent.position)
  
imports: from typing import Any, Dict, List, Optional
  import numpy
  import opytimizer.math.distribution
  import opytimizer.math.random
  import opytimizer.utils.exception
  from opytimizer.core import Optimizer
  from opytimizer.core.function import Function
  from opytimizer.core.space import Space
  from opytimizer.utils import logging
label_desc: Use image node_id 11 for calling the SBO obj's underlying member method code with example usage: obj.update(space, function) without return types