#!/usr/bin/env python
# Created by "Thieu" at 17:55, 21/05/2022 ----------%
# Email: nguyenthieu2102@gmail.com %
# Github: https://github.com/thieu1995 %
# --------------------------------------------------%
import numpy as np
from mealpy.optimizer import Optimizer
[docs]class OriginalHBA(Optimizer):
"""
The original version of: Honey Badger Algorithm (HBA)
Links:
1. https://www.sciencedirect.com/science/article/abs/pii/S0378475421002901
2. https://www.mathworks.com/matlabcentral/fileexchange/98204-honey-badger-algorithm
Examples
~~~~~~~~
>>> import numpy as np
>>> from mealpy import FloatVar, HBA
>>>
>>> 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 = HBA.OriginalHBA(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] Hashim, F. A., Houssein, E. H., Hussain, K., Mabrouk, M. S., & Al-Atabany, W. (2022). Honey Badger Algorithm: New metaheuristic
algorithm for solving optimization problems. Mathematics and Computers in Simulation, 192, 84-110.
"""
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 initialize_variables(self):
self.beta = 6 # the ability of HB to get the food Eq.(4)
self.C = 2 # constant in Eq. (3)
[docs] def get_intensity__(self, best, pop):
size = len(pop)
di = np.zeros(size)
si = np.zeros(size)
for idx in range(0, size):
di[idx] = (np.linalg.norm(pop[idx].solution - best.solution) + self.EPSILON) ** 2
if idx == size - 1:
si[idx] = (np.linalg.norm(pop[idx].solution - self.pop[0].solution) + self.EPSILON) ** 2
else:
si[idx] = (np.linalg.norm(pop[idx].solution - self.pop[idx + 1].solution) + self.EPSILON) ** 2
r2 = self.generator.random(size)
return r2 * si / (4 * np.pi * di)
[docs] def evolve(self, epoch):
"""
The main operations (equations) of algorithm. Inherit from Optimizer class
Args:
epoch (int): The current iteration
"""
tt = self.epoch
alpha= self.C * np.exp(-tt/self.epoch) # density factor in Eq. (3)
I = self.get_intensity__(self.g_best, self.pop) # intensity in Eq. (2)
pop_new = []
for idx in range(0, self.pop_size):
r = self.generator.random()
F = self.generator.choice([1, -1])
di = self.g_best.solution - self.pop[idx].solution
r3 = self.generator.random(self.problem.n_dims)
r4 = self.generator.random(self.problem.n_dims)
r5 = self.generator.random(self.problem.n_dims)
r6 = self.generator.random(self.problem.n_dims)
r7 = self.generator.random(self.problem.n_dims)
temp1 = self.g_best.solution + F * self.beta * I[idx] * self.g_best.solution + F*r3*alpha*di*np.abs(np.cos(2*np.pi*r4) * (1 - np.cos(2*np.pi*r5)))
temp2 = self.g_best.solution + F * r7 * alpha * di
pos_new = np.where(r6 < 0.5, temp1, temp2)
pos_new = self.correct_solution(pos_new)
agent = self.generate_empty_agent(pos_new)
pop_new.append(agent)
if self.mode not in self.AVAILABLE_MODES:
agent.target = self.get_target(pos_new)
self.pop[idx] = self.get_better_agent(agent, self.pop[idx], self.problem.minmax)
if self.mode in self.AVAILABLE_MODES:
pop_new = self.update_target_for_population(pop_new)
self.pop = self.greedy_selection_population(self.pop, pop_new, self.problem.minmax)