# !/usr/bin/env python
# Created by "Thieu" at 14:51, 17/03/2020 ----------%
# Email: nguyenthieu2102@gmail.com %
# Github: https://github.com/thieu1995 %
# --------------------------------------------------%
import numpy as np
from math import gamma
from copy import deepcopy
from mealpy.optimizer import Optimizer
[docs]class BaseHHO(Optimizer):
"""
The original version of: Harris Hawks Optimization (HHO)
Links:
1. https://doi.org/10.1016/j.future.2019.02.028
Examples
~~~~~~~~
>>> import numpy as np
>>> from mealpy.swarm_based.HHO import BaseHHO
>>>
>>> def fitness_function(solution):
>>> return np.sum(solution**2)
>>>
>>> problem_dict1 = {
>>> "fit_func": fitness_function,
>>> "lb": [-10, -15, -4, -2, -8],
>>> "ub": [10, 15, 12, 8, 20],
>>> "minmax": "min",
>>> "verbose": True,
>>> }
>>>
>>> epoch = 1000
>>> pop_size = 50
>>> model = BaseHHO(problem_dict1, epoch, pop_size)
>>> best_position, best_fitness = model.solve()
>>> print(f"Solution: {best_position}, Fitness: {best_fitness}")
References
~~~~~~~~~~
[1] Heidari, A.A., Mirjalili, S., Faris, H., Aljarah, I., Mafarja, M. and Chen, H., 2019.
Harris hawks optimization: Algorithm and applications. Future generation computer systems, 97, pp.849-872.
"""
def __init__(self, problem, epoch=10000, pop_size=100, **kwargs):
"""
Args:
problem (dict): The problem dictionary
epoch (int): maximum number of iterations, default = 10000
pop_size (int): number of population size, default = 100
"""
super().__init__(problem, kwargs)
self.nfe_per_epoch = 1.5 * pop_size
self.sort_flag = False
self.epoch = epoch
self.pop_size = pop_size
[docs] def evolve(self, epoch):
"""
The main operations (equations) of algorithm. Inherit from Optimizer class
Args:
epoch (int): The current iteration
"""
pop_new = []
for idx in range(0, self.pop_size):
# -1 < E0 < 1
E0 = 2 * np.random.uniform() - 1
# factor to show the decreasing energy of rabbit
E = 2 * E0 * (1 - (epoch + 1) * 1.0 / self.epoch)
J = 2 * (1 - np.random.uniform())
# -------- Exploration phase Eq. (1) in paper -------------------
if np.abs(E) >= 1:
# Harris' hawks perch randomly based on 2 strategy:
if np.random.rand() >= 0.5: # perch based on other family members
X_rand = deepcopy(self.pop[np.random.randint(0, self.pop_size)][self.ID_POS])
pos_new = X_rand - np.random.uniform() * np.abs(X_rand - 2 * np.random.uniform() * self.pop[idx][self.ID_POS])
else: # perch on a random tall tree (random site inside group's home range)
X_m = np.mean([x[self.ID_POS] for x in self.pop])
pos_new = (self.g_best[self.ID_POS] - X_m) - np.random.uniform() * \
(self.problem.lb + np.random.uniform() * (self.problem.ub - self.problem.lb))
pos_new = self.amend_position(pos_new)
pop_new.append([pos_new, None])
# -------- Exploitation phase -------------------
else:
# Attacking the rabbit using 4 strategies regarding the behavior of the rabbit
# phase 1: ----- surprise pounce (seven kills) ----------
# surprise pounce (seven kills): multiple, short rapid dives by different hawks
if (np.random.rand() >= 0.5):
delta_X = self.g_best[self.ID_POS] - self.pop[idx][self.ID_POS]
if np.abs(E) >= 0.5: # Hard besiege Eq. (6) in paper
pos_new = delta_X - E * np.abs(J * self.g_best[self.ID_POS] - self.pop[idx][self.ID_POS])
else: # Soft besiege Eq. (4) in paper
pos_new = self.g_best[self.ID_POS] - E * np.abs(delta_X)
pos_new = self.amend_position(pos_new)
pop_new.append([pos_new, None])
else:
xichma = np.power((gamma(1 + 1.5) * np.sin(np.pi * 1.5 / 2.0)) /
(gamma((1 + 1.5) * 1.5 * np.power(2, (1.5 - 1) / 2)) / 2.0), 1.0 / 1.5)
LF_D = 0.01 * np.random.uniform() * xichma / np.power(np.abs(np.random.uniform()), 1.0 / 1.5)
if np.abs(E) >= 0.5: # Soft besiege Eq. (10) in paper
Y = self.g_best[self.ID_POS] - E * np.abs(J * self.g_best[self.ID_POS] - self.pop[idx][self.ID_POS])
else: # Hard besiege Eq. (11) in paper
X_m = np.mean([x[self.ID_POS] for x in self.pop])
Y = self.g_best[self.ID_POS] - E * np.abs(J * self.g_best[self.ID_POS] - X_m)
pos_Y = self.amend_position(Y)
fit_Y = self.get_fitness_position(pos_Y)
Z = Y + np.random.uniform(self.problem.lb, self.problem.ub) * LF_D
pos_Z = self.amend_position(Z)
fit_Z = self.get_fitness_position(pos_Z)
if self.compare_agent([pos_Y, fit_Y], self.pop[idx]):
pop_new.append([pos_Y, fit_Y])
continue
if self.compare_agent([pos_Z, fit_Z], self.pop[idx]):
pop_new.append([pos_Z, fit_Z])
continue
pop_new.append(deepcopy(self.pop[idx]))
self.pop = self.update_fitness_population(pop_new)