from hyponic.optimizers.base_optimizer import BaseOptimizer
import numpy as np
import numexpr as ne
[docs]class CS(BaseOptimizer):
"""
Cuckoo Search (CS) algorithm
Hyperparameters:
+ pa(float), default=0.25: probability of cuckoo's egg to be discovered
+ alpha(float), default=0.5: step size
+ k(float), default=1: Levy multiplication coefficient
Example
~~~~~~~
>>> from hyponic.optimizers.swarm_based.CS import CS
>>> 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'
>>> }
>>>
>>> cs = CS(epoch=40, population_size=100, verbose=True, early_stopping=4)
>>> cs.solve(problem_dict)
>>> print(cs.get_best_score())
>>> print(cs.get_best_solution())
"""
def __init__(self, epoch: int = 10, population_size: int = 10, minmax: str = None, verbose: bool = False,
pa: float = 0.25, alpha: float = 0.5, k: float = 1,
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 pa: probability of cuckoo's egg to be discovered
:param alpha: step size
:param k: Levy multiplication coefficient
"""
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.pa = pa
self.alpha = alpha
self.k = k
self.nests = None
self.nests_fitness = None
self.cuckoo_coords = None
def _before_initialization(self):
super()._before_initialization()
if isinstance(self.pa, float) is False and isinstance(self.pa, int) is False:
raise TypeError('pa should be a float or an integer')
if isinstance(self.alpha, float) is False and isinstance(self.alpha, int) is False:
raise TypeError('alpha should be a float or an integer')
if isinstance(self.k, float) is False and isinstance(self.k, int) is False:
raise TypeError('k should be a float or an integer')
[docs] def initialize(self, problem_dict):
super().initialize(problem_dict)
self.nests = self.coords
self.nests_fitness = np.array([self.function(self.nests[i]) for i in range(self.population_size)])
self.cuckoo_coords = np.random.uniform(self.lb, self.ub, self.dimensions)
def _levy_flight(self, x):
u = np.random.normal(0, 1, size=self.dimensions)
v = np.random.normal(0, 1, size=self.dimensions)
best_coords = self.nests[self._argminmax()(self.nests_fitness)]
return ne.evaluate('x + k * u / (abs(v) ** (1 / 1.5)) * (best_coords - x)', local_dict={
'x': x, 'k': self.k, 'u': u, 'v': v, 'best_coords': best_coords
})
[docs] def evolve(self, current_epoch):
x_new = self._levy_flight(self.cuckoo_coords)
self.cuckoo_coords = np.clip(x_new, self.lb, self.ub)
next_nest = np.random.randint(0, self.population_size)
new_fitness = self.function(self.cuckoo_coords)
if new_fitness < self.nests_fitness[next_nest]:
self.nests[next_nest] = self.cuckoo_coords
self.nests_fitness[next_nest] = new_fitness
number_of_discovered_eggs = int(self.pa * self.population_size)
worst_nests = np.argsort(self.nests_fitness)[-number_of_discovered_eggs:]
self.nests[worst_nests] = np.random.uniform(self.lb, self.ub, (number_of_discovered_eggs, self.dimensions))
self.nests_fitness[worst_nests] = np.array([self.function(self.nests[i]) for i in worst_nests])
[docs] def get_best_score(self):
return self._minmax()(self.nests_fitness)
[docs] def get_best_solution(self):
return self.nests[self._argminmax()(self.nests_fitness)]
[docs] def get_current_best_score(self):
return self.get_best_score()
[docs] def get_current_best_solution(self):
return self.get_best_solution()