from hyponic.optimizers.base_optimizer import BaseOptimizer
import numpy as np
import numexpr as ne
[docs]class ABC(BaseOptimizer):
"""
Artificial Bee Colony (ABC) algorithm
Hyperparameters:
+ limits(int), default=25: the number of trials before abandoning food source
Example
~~~~~~~
>>> from hyponic.optimizers.swarm_based.ABC import ABC
>>> import numpy as np
>>>
>>> def sphere(x):
>>> return np.sum(x ** 2)
>>>
>>> problem_dict = {
>>> 'fit_func': sphere,
>>> 'lb': [-5.12, -5, -14, -6, -0.9],
>>> 'ub': [5.12, 5, 14, 6, 0.9],
>>> 'minmax': 'min'
>>> }
>>>
>>> abc = ABC(epoch=40, population_size=100, verbose=True, early_stopping=4)
>>> abc.solve(problem_dict)
>>> print(abc.get_best_score())
>>> print(abc.get_best_solution())
"""
def __init__(self, epoch: int = 10, population_size: int = 10, minmax: str = None, limits=25,
verbose: bool = False, mode: str = 'single', n_workers: int = 4, early_stopping: int | None = None,
**kwargs):
"""
:param epoch: number of iterations
:param population_size: number of individuals in the population
:param minmax: 'min' or 'max', depending on whether the problem is a minimization or maximization problem
:param verbose: whether to print the progress, default is False
:param mode: 'single' or 'multithread', depending on whether to use multithreading or not
:param n_workers: number of workers to use in multithreading mode
:param early_stopping: number of epochs to wait before stopping the optimization process. If None, then early
stopping is not used
:param limits: the number of trials before abandoning food source
"""
super().__init__(**kwargs)
self.epoch = epoch
self.population_size = population_size
self.minmax = minmax
self.verbose = verbose
self.mode = mode
self.n_workers = n_workers
self.early_stopping = early_stopping
self.limits = limits
self.fitness = None
self.g_best = None
self.g_best_coords = None
self.trials = None
def _before_initialization(self):
super()._before_initialization()
if not isinstance(self.limits, int) or self.limits < 1:
raise ValueError("limits should be a positive integer")
[docs] def initialize(self, problem_dict):
super().initialize(problem_dict)
self.g_best = np.inf if self._minmax() == min else -np.inf
self.fitness = np.array([self.function(self.coords[i]) for i in range(self.population_size)])
self.trials = np.zeros(self.population_size)
def _coordinate_update_phase(self, i, k):
phi = np.random.uniform(-1, 1, self.dimensions)
new_coords = ne.evaluate("coords + phi * (coords - new_coords)", local_dict={'coords': self.coords[i],
'phi': phi,
'new_coords': self.coords[k]})
new_coords = np.clip(new_coords, self.lb, self.ub)
new_fitness = self.function(new_coords)
if self._minmax()(np.array([self.fitness[i], new_fitness])) != self.fitness[i]:
self.coords[i] = new_coords
self.fitness[i] = new_fitness
self.trials[i] = 0
else:
self.trials[i] += 1
def _employed_bees_phase(self):
for i in range(self.population_size):
k = np.random.choice([j for j in range(self.population_size) if j != i])
self._coordinate_update_phase(i, k)
def _onlooker_bees_phase(self):
if np.all(self.fitness == 0):
probabilities = np.ones(self.population_size) / self.population_size
else:
probabilities = self.fitness / np.sum(self.fitness)
for i in range(self.population_size):
k = np.random.choice([j for j in range(self.population_size)], p=probabilities)
self._coordinate_update_phase(i, k)
def _scout_bees_phase(self):
for i in range(self.population_size):
if self.trials[i] > self.limits:
self.coords[i] = np.random.uniform(self.lb, self.ub, self.dimensions)
self.fitness[i] = self.function(self.coords[i])
self.trials[i] = 0
[docs] def evolve(self, epoch):
self._employed_bees_phase()
self._onlooker_bees_phase()
self._scout_bees_phase()
best_index = self._argminmax()(self.fitness)
self.g_best = self.fitness[best_index]
self.g_best_coords = self.coords[best_index]
[docs] def get_best_score(self):
return self.g_best
[docs] def get_best_solution(self):
return self.g_best_coords
[docs] def get_current_best_score(self):
return self._minmax()(self.fitness)
[docs] def get_current_best_solution(self):
best_index = self._argminmax()(self.fitness)
return self.coords[best_index]