class_docstr: An HHO class, inherited from Optimizer.
  
  This is the designed class to define HHO-related
  variables and methods.
  
  References:
      A. Heidari et al. Harris hawks optimization: Algorithm and applications.
      Future Generation Computer Systems (2019).
docstr: Calculates the initial coefficients, i.e., energy and jump's strength.
  
  Args:
      iteration: Current iteration.
      n_iterations: Maximum number of iterations.
  
  Returns:
      (Tuple[float, float]): Absolute value of energy and jump's strength.
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code: def _calculate_initial_coefficients(
      self, iteration: int, n_iterations: int
  ) -> Tuple[float, float]:
      """Calculates the initial coefficients, i.e., energy and jump's strength.
  
      Args:
          iteration: Current iteration.
          n_iterations: Maximum number of iterations.
  
      Returns:
          (Tuple[float, float]): Absolute value of energy and jump's strength.
  
      """
  
      r1 = r.generate_uniform_random_number()
  
      E_0 = 2 * r1 - 1
      J = 2 * (1 - r1)
  
      # Calculates the energy (eq. 3)
      E = 2 * E_0 * (1 - (iteration / n_iterations))
  
      return np.fabs(E), J
  
file_path: opytimizer/opytimizer/optimizers/population/hho.py
imports: from typing import Any, Dict, List, Optional, Tuple
  import numpy
  import opytimizer.math.distribution
  import opytimizer.math.random
  from opytimizer.core import Optimizer
  from opytimizer.core.agent import Agent
  from opytimizer.core.function import Function
  from opytimizer.core.space import Space
  from opytimizer.utils import logging
label_desc: Use image node_id 2 for calling the HHO obj's underlying member method code with example usage: obj._calculate_initial_coefficients(iteration, n_iterations) and returns: np,  J