Source code for niapy.algorithms.basic.hho
# encoding=utf8
import logging
import numpy as np
from niapy.algorithms.algorithm import Algorithm
from niapy.util import levy_flight
logging.basicConfig()
logger = logging.getLogger('niapy.algorithms.basic')
logger.setLevel('INFO')
__all__ = ['HarrisHawksOptimization']
[docs]class HarrisHawksOptimization(Algorithm):
r"""Implementation of Harris Hawks Optimization algorithm.
Algorithm:
Harris Hawks Optimization
Date:
2020
Authors:
Francisco Jose Solis-Munoz
License:
MIT
Reference paper:
Heidari et al. "Harris hawks optimization: Algorithm and applications". Future Generation Computer Systems. 2019. Vol. 97. 849-872.
Attributes:
Name (List[str]): List of strings representing algorithm name.
levy (float): Levy factor.
See Also:
* :class:`niapy.algorithms.Algorithm`
"""
Name = ['HarrisHawksOptimization', 'HHO']
[docs] def __init__(self, population_size=40, levy=0.01, *args, **kwargs):
"""Initialize HarrisHawksOptimization.
Args:
population_size (Optional[int]): Population size.
levy (Optional[float]): Levy factor.
"""
super().__init__(population_size, *args, **kwargs)
self.levy = levy
[docs] @staticmethod
def info():
r"""Get algorithms information.
Returns:
str: Algorithm information.
See Also:
* :func:`niapy.algorithms.Algorithm.info`
"""
return r"""Heidari et al. "Harris hawks optimization: Algorithm and applications". Future Generation Computer Systems. 2019. Vol. 97. 849-872."""
[docs] def set_parameters(self, population_size=40, levy=0.01, **kwargs):
r"""Set the parameters of the algorithm.
Args:
population_size (Optional[int]): Population size.
levy (Optional[float]): Levy factor.
See Also:
* :func:`niapy.algorithms.Algorithm.set_parameters`
"""
super().set_parameters(population_size=population_size, **kwargs)
self.levy = levy
[docs] def get_parameters(self):
r"""Get parameters of the algorithm.
Returns:
Dict[str, Any]: Algorithm parameters.
"""
d = super().get_parameters()
d.update({
'levy': self.levy
})
return d
[docs] def run_iteration(self, task, population, population_fitness, best_x, best_fitness, **params):
r"""Core function of Harris Hawks Optimization.
Args:
task (Task): Optimization task.
population (numpy.ndarray): Current population
population_fitness (numpy.ndarray[float]): Current population fitness/function values
best_x (numpy.ndarray): Current best individual
best_fitness (float): Current best individual function/fitness value
params (Dict[str, Any]): Additional algorithm arguments
Returns:
Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray, float, Dict[str, Any]]:
1. New population
2. New population fitness/function values
3. New global best solution
4. New global best fitness/objective value
"""
# Decreasing energy factor
decreasing_energy_factor = 2 * (1 - (task.iters + 1) / task.max_iters)
mean_sol = np.mean(population)
# Update population
for i in range(self.population_size):
jumping_energy = self.rng.uniform(0, 2)
decreasing_energy_random = self.rng.uniform(-1, 1)
escaping_energy = decreasing_energy_factor * decreasing_energy_random
escaping_energy_abs = np.abs(escaping_energy)
random_number = self.rng.random()
if escaping_energy >= 1 and random_number >= 0.5:
# 0. Exploration: Random tall tree
rhi = self.rng.integers(self.population_size)
random_agent = population[rhi]
population[i] = random_agent - self.rng.random() * np.abs(random_agent - 2 * self.rng.random() * population[i])
elif escaping_energy_abs >= 1 and random_number < 0.5:
# 1. Exploration: Family members mean
population[i] = (best_x - mean_sol) - self.rng.random() * self.rng.uniform(task.lower, task.upper)
elif escaping_energy_abs >= 0.5 and random_number >= 0.5:
# 2. Exploitation: Soft besiege
population[i] = \
(best_x - population[i]) - \
escaping_energy * \
np.abs(jumping_energy * best_x - population[i])
elif escaping_energy_abs < 0.5 <= random_number:
# 3. Exploitation: Hard besiege
population[i] = \
best_x - \
escaping_energy * \
np.abs(best_x - population[i])
elif escaping_energy_abs >= 0.5 > random_number:
# 4. Exploitation: Soft besiege with progressive rapid dives
cand1 = task.repair(best_x - escaping_energy * np.abs(jumping_energy * best_x - population[i]), rng=self.rng)
random_vector = self.rng.random(task.dimension)
cand2 = task.repair(cand1 + random_vector * levy_flight(alpha=self.levy, size=task.dimension, rng=self.rng),
rng=self.rng)
if task.eval(cand1) < population_fitness[i]:
population[i] = cand1
elif task.eval(cand2) < population_fitness[i]:
population[i] = cand2
elif escaping_energy_abs < 0.5 and random_number < 0.5:
# 5. Exploitation: Hard besiege with progressive rapid dives
cand1 = task.repair(best_x - escaping_energy * np.abs(jumping_energy * best_x - mean_sol), rng=self.rng)
random_vector = self.rng.random(task.dimension)
cand2 = task.repair(cand1 + random_vector * levy_flight(alpha=self.levy, size=task.dimension, rng=self.rng),
rng=self.rng)
if task.eval(cand1) < population_fitness[i]:
population[i] = cand1
elif task.eval(cand2) < population_fitness[i]:
population[i] = cand2
# Repair agent (from population) values
population[i] = task.repair(population[i], rng=self.rng)
# Eval population
population_fitness[i] = task.eval(population[i])
# Get best of population
best_index = np.argmin(population_fitness)
xb_cand = population[best_index].copy()
fxb_cand = population_fitness[best_index].copy()
if fxb_cand < best_fitness:
best_fitness = fxb_cand
best_x = xb_cand.copy()
return population, population_fitness, best_x, best_fitness, {}
