# encoding=utf8
import logging
from niapy.algorithms.algorithm import Algorithm
logging.basicConfig()
logger = logging.getLogger('niapy.algorithms.other')
logger.setLevel('INFO')
__all__ = ['HillClimbAlgorithm']
def neighborhood(x, delta, task, rng):
r"""Get neighbours of point.
Args:
x (numpy.ndarray): Point.
delta (float): Standard deviation.
task (Task): Optimization task.
rng (numpy.random.Generator): Random generator.
Returns:
Tuple[numpy.ndarray, float]:
1. New solution.
2. New solutions function/fitness value.
"""
new_x = x + rng.normal(0, delta, task.dimension)
new_x = task.repair(new_x, rng)
new_x_fitness = task.eval(new_x)
return new_x, new_x_fitness
[docs]class HillClimbAlgorithm(Algorithm):
r"""Implementation of iterative hill climbing algorithm.
Algorithm:
Hill Climbing Algorithm
Date:
2018
Authors:
Jan Popič
License:
MIT
Reference URL:
Reference paper:
See Also:
* :class:`niapy.algorithms.Algorithm`
Attributes:
delta (float): Change for searching in neighborhood.
neighborhood_function (Callable[numpy.ndarray, float, Task], Tuple[numpy.ndarray, float]]): Function for getting neighbours.
"""
Name = ['HillClimbAlgorithm', 'HC']
[docs] @staticmethod
def info():
r"""Get basic information about the algorithm.
Returns:
str: Basic information.
See Also:
:func:`niapy.algorithms.algorithm.Algorithm.info`
"""
return r"""TODO"""
[docs] def __init__(self, delta=0.5, neighborhood_function=neighborhood, *args, **kwargs):
"""Initialize HillClimbAlgorithm.
Args:
* delta (Optional[float]): Change for searching in neighborhood.
* neighborhood_function (Optional[Callable[numpy.ndarray, float, Task], Tuple[numpy.ndarray, float]]]): Function for getting neighbours.
"""
kwargs.pop('population_size', None)
super().__init__(1, *args, **kwargs)
self.delta = delta
self.neighborhood_function = neighborhood_function
[docs] def set_parameters(self, delta=0.5, neighborhood_function=neighborhood, **kwargs):
r"""Set the algorithm parameters/arguments.
Args:
* delta (Optional[float]): Change for searching in neighborhood.
* neighborhood_function (Optional[Callable[numpy.ndarray, float, Task], Tuple[numpy.ndarray, float]]]): Function for getting neighbours.
"""
kwargs.pop('population_size', None)
super().set_parameters(population_size=1, **kwargs)
self.delta = delta
self.neighborhood_function = neighborhood_function
[docs] def get_parameters(self):
d = super().get_parameters()
d.pop('population_size', None)
d.update({
'delta': self.delta,
'neighborhood_function': self.neighborhood_function
})
return d
[docs] def init_population(self, task):
r"""Initialize stating point.
Args:
task (Task): Optimization task.
Returns:
Tuple[numpy.ndarray, float, Dict[str, Any]]:
1. New individual.
2. New individual function/fitness value.
3. Additional arguments.
"""
x = task.lower + self.random(task.dimension) * task.range
return x, task.eval(x), {}
[docs] def run_iteration(self, task, x, fx, best_x, best_fitness, **params):
r"""Core function of HillClimbAlgorithm algorithm.
Args:
task (Task): Optimization task.
x (numpy.ndarray): Current solution.
fx (float): Current solutions fitness/function value.
best_x (numpy.ndarray): Global best solution.
best_fitness (float): Global best solutions function/fitness value.
**params (Dict[str, Any]): Additional arguments.
Returns:
Tuple[numpy.ndarray, float, numpy.ndarray, float, Dict[str, Any]]:
1. New solution.
2. New solutions function/fitness value.
3. Additional arguments.
"""
lo, xn = False, task.lower + task.range * self.random(task.dimension)
xn_f = task.eval(xn)
while not lo:
yn, yn_f = self.neighborhood_function(x, self.delta, task, rng=self.rng)
if yn_f < xn_f:
xn, xn_f = yn, yn_f
else:
lo = True or task.stopping_condition()
best_x, best_fitness = self.get_best(xn, xn_f, best_x, best_fitness)
return xn, xn_f, best_x, best_fitness, {}
