#!/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 mealpy.optimizer import Optimizer
[docs]class OriginalHHO(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 import FloatVar, HHO
>>>
>>> def objective_function(solution):
>>> return np.sum(solution**2)
>>>
>>> problem_dict = {
>>> "bounds": FloatVar(n_vars=30, lb=(-10.,) * 30, ub=(10.,) * 30, name="delta"),
>>> "minmax": "min",
>>> "obj_func": objective_function
>>> }
>>>
>>> model = HHO.OriginalHHO(epoch=1000, pop_size=50)
>>> g_best = model.solve(problem_dict)
>>> print(f"Solution: {g_best.solution}, Fitness: {g_best.target.fitness}")
>>> print(f"Solution: {model.g_best.solution}, Fitness: {model.g_best.target.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, epoch: int = 10000, pop_size: int = 100, **kwargs: object) -> None:
"""
Args:
epoch (int): maximum number of iterations, default = 10000
pop_size (int): number of population size, default = 100
"""
super().__init__(**kwargs)
self.epoch = self.validator.check_int("epoch", epoch, [1, 100000])
self.pop_size = self.validator.check_int("pop_size", pop_size, [5, 10000])
self.set_parameters(["epoch", "pop_size"])
self.sort_flag = False
[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 * self.generator.uniform() - 1
# factor to show the decreasing energy of rabbit
E = 2 * E0 * (1. - epoch * 1.0 / self.epoch)
J = 2 * (1 - self.generator.uniform())
# -------- Exploration phase Eq. (1) in paper -------------------
if np.abs(E) >= 1:
# Harris' hawks perch randomly based on 2 strategy:
if self.generator.random() >= 0.5: # perch based on other family members
X_rand = self.pop[self.generator.integers(0, self.pop_size)].solution.copy()
pos_new = X_rand - self.generator.uniform() * np.abs(X_rand - 2 * self.generator.uniform() * self.pop[idx].solution)
else: # perch on a random tall tree (random site inside group's home range)
X_m = np.mean([x.solution for x in self.pop])
pos_new = (self.g_best.solution - X_m) - self.generator.uniform() * \
(self.problem.lb + self.generator.uniform() * (self.problem.ub - self.problem.lb))
pos_new = self.correct_solution(pos_new)
agent = self.generate_empty_agent(pos_new)
pop_new.append(agent)
# -------- 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 (self.generator.random() >= 0.5):
delta_X = self.g_best.solution - self.pop[idx].solution
if np.abs(E) >= 0.5: # Hard besiege Eq. (6) in paper
pos_new = delta_X - E * np.abs(J * self.g_best.solution - self.pop[idx].solution)
else: # Soft besiege Eq. (4) in paper
pos_new = self.g_best.solution - E * np.abs(delta_X)
pos_new = self.correct_solution(pos_new)
agent = self.generate_empty_agent(pos_new)
pop_new.append(agent)
else:
LF_D = self.get_levy_flight_step(beta=1.5, multiplier=0.01, case=-1)
if np.abs(E) >= 0.5: # Soft besiege Eq. (10) in paper
Y = self.g_best.solution - E * np.abs(J * self.g_best.solution - self.pop[idx].solution)
else: # Hard besiege Eq. (11) in paper
X_m = np.mean([x.solution for x in self.pop])
Y = self.g_best.solution - E * np.abs(J * self.g_best.solution - X_m)
pos_Y = self.correct_solution(Y)
target_Y = self.get_target(pos_Y)
Z = Y + self.generator.uniform(self.problem.lb, self.problem.ub) * LF_D
pos_Z = self.correct_solution(Z)
target_Z = self.get_target(pos_Z)
if self.compare_target(target_Y, self.pop[idx].target, self.problem.minmax):
agent = self.generate_empty_agent(pos_Y)
agent.target = target_Y
pop_new.append(agent)
continue
if self.compare_target(target_Z, self.pop[idx].target, self.problem.minmax):
agent = self.generate_empty_agent(pos_Z)
agent.target = target_Z
pop_new.append(agent)
continue
pop_new.append(self.pop[idx].copy())
if self.mode not in self.AVAILABLE_MODES:
for idx, agent in enumerate(pop_new):
pop_new[idx].target = self.get_target(agent.solution)
else:
pop_new = self.update_target_for_population(pop_new)
self.pop = self.greedy_selection_population(self.pop, pop_new, self.problem.minmax)