from hyponic.optimizers.base_optimizer import BaseOptimizer
import numpy as np
import numexpr as ne
[docs]class GWO(BaseOptimizer):
"""
Grey Wolf Optimization (GWO) algorithm
Example
~~~~~~~
>>> from hyponic.optimizers.swarm_based.GWO import GWO
>>> 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'
>>> }
>>>
>>> gwo = GWO(epoch=40, population_size=100, verbose=True, early_stopping=4)
>>> gwo.solve(problem_dict)
>>> print(gwo.get_best_score())
>>> print(gwo.get_best_solution())
"""
def __init__(self, epoch: int = 10, population_size: int = 10, minmax: str = None, 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
"""
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.fitness = None
self.g_best = None
self.g_best_coords = None
[docs] def initialize(self, problem_dict):
super().initialize(problem_dict)
self.g_best = np.inf if self.minmax == 'min' else -np.inf
self.g_best_coords = np.zeros(self.dimensions)
self.fitness = np.array([self.function(x) for x in self.coords], dtype=np.float64)
[docs] def evolve(self, current_epoch):
a = 2 - current_epoch * (2 / self.epoch)
for i in range(self.population_size):
r1 = np.random.rand()
r2 = np.random.rand()
A = 2 * a * r1 - a
C = 2 * r2
D = np.abs(C * self.coords[np.random.randint(0, self.population_size)] - self.coords[np.random.randint(0, self.population_size)])
coords_new = self.coords[i] + A * D
fitness_new = self.function(coords_new)
if self._minmax()([fitness_new, self.fitness[i]]) == fitness_new:
self.coords[i] = coords_new
self.fitness[i] = fitness_new
if self._minmax()([fitness_new, self.g_best]) == fitness_new:
self.g_best = fitness_new
self.g_best_coords = coords_new
[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):
return self.coords[self._argminmax()(self.fitness)]