# coding=utf-8
import logging
import numpy as np
from niapy.algorithms.algorithm import Algorithm
__all__ = ['BeesAlgorithm']
logging.basicConfig()
logger = logging.getLogger('niapy.algorithms.basic')
logger.setLevel('INFO')
[docs]class BeesAlgorithm(Algorithm):
r"""Implementation of Bees algorithm.
Algorithm:
The Bees algorithm
Date:
2019
Authors:
Rok Potočnik
License:
MIT
Reference paper:
DT Pham, A Ghanbarzadeh, E Koc, S Otri, S Rahim, and M Zaidi. The bees algorithm-a novel tool for complex optimisation problems. In Proceedings of the 2nd Virtual International Conference on Intelligent Production Machines and Systems (IPROMS 2006), pages 454–459, 2006
Attributes:
population_size (Optional[int]): Number of scout bees parameter.
m (Optional[int]): Number of sites selected out of n visited sites parameter.
e (Optional[int]): Number of best sites out of m selected sites parameter.
nep (Optional[int]): Number of bees recruited for best e sites parameter.
nsp (Optional[int]): Number of bees recruited for the other selected sites parameter.
ngh (Optional[float]): Initial size of patches parameter.
See Also:
* :func:`niapy.algorithms.Algorithm.set_parameters`
"""
Name = ['BeesAlgorithm', 'BEA']
[docs] @staticmethod
def info():
r"""Get information about algorithm.
Returns:
str: Algorithm information
See Also:
* :func:`niapy.algorithms.Algorithm.info`
"""
return r"""DT Pham, A Ghanbarzadeh, E Koc, S Otri, S Rahim, and M Zaidi. The bees algorithm-a novel tool for complex optimisation problems. In Proceedings of the 2nd Virtual International Conference on Intelligent Production Machines and Systems (IPROMS 2006), pages 454–459, 2006"""
[docs] def __init__(self, population_size=40, m=5, e=4, ngh=1, nep=4, nsp=2, *args, **kwargs):
"""Initialize BeesAlgorithm.
Args:
population_size (Optional[int]): Number of scout bees parameter.
m (Optional[int]): Number of sites selected out of n visited sites parameter.
e (Optional[int]): Number of best sites out of m selected sites parameter.
nep (Optional[int]): Number of bees recruited for best e sites parameter.
nsp (Optional[int]): Number of bees recruited for the other selected sites parameter.
ngh (Optional[float]): Initial size of patches parameter.
See Also:
* :func:`niapy.algorithms.Algorithm.__init__`
"""
super().__init__(population_size, *args, **kwargs)
self.m = m
self.e = e
self.ngh = ngh
self.nep = nep
self.nsp = nsp
[docs] def set_parameters(self, population_size=40, m=5, e=4, ngh=1, nep=4, nsp=2, **kwargs):
r"""Set the parameters of the algorithm.
Args:
population_size (Optional[int]): Number of scout bees parameter.
m (Optional[int]): Number of sites selected out of n visited sites parameter.
e (Optional[int]): Number of best sites out of m selected sites parameter.
nep (Optional[int]): Number of bees recruited for best e sites parameter.
nsp (Optional[int]): Number of bees recruited for the other selected sites parameter.
ngh (Optional[float]): Initial size of patches parameter.
See Also:
* :func:`niapy.algorithms.Algorithm.set_parameters`
"""
super().set_parameters(population_size=population_size, **kwargs)
self.m = m
self.e = e
self.ngh = ngh
self.nep = nep
self.nsp = nsp
[docs] def get_parameters(self):
r"""Get parameters of the algorithm.
Returns:
Dict[str, Any]: Algorithm Parameters.
"""
d = super().get_parameters()
d.update({
'm': self.m,
'e': self.e,
'ngh': self.ngh,
'nep': self.nep,
'nsp': self.nsp
})
return d
[docs] def bee_dance(self, x, task, ngh):
r"""Bees Dance. Search for new positions.
Args:
x (numpy.ndarray): One individual from the population.
task (Task): Optimization task.
ngh (float): A small value for patch search.
Returns:
Tuple[numpy.ndarray, float]:
1. New individual.
2. New individual fitness/function values.
"""
ind = self.integers(task.dimension)
y = x.copy()
y[ind] = x[ind] + self.uniform(-ngh, ngh)
y = task.repair(y)
y_fitness = task.eval(y)
return y, y_fitness
[docs] def init_population(self, task):
r"""Initialize the starting population.
Args:
task (Task): Optimization task
Returns:
Tuple[numpy.ndarray, numpy.ndarray, Dict[str, Any]]:
1. New population.
2. New population fitness/function values.
See Also:
* :func:`niapy.algorithms.Algorithm.init_population`
"""
population, population_fitness, _ = super().init_population(task)
sorted_indices = np.argsort(population_fitness)
population_fitness = population_fitness[sorted_indices]
population = population[sorted_indices, :]
return population, population_fitness, {'ngh': self.ngh}
[docs] def run_iteration(self, task, population, population_fitness, best_x, best_fitness, **params):
r"""Core function of Forest Optimization Algorithm.
Args:
task (Task): Optimization task.
population (numpy.ndarray[float]): Current population.
population_fitness (numpy.ndarray[float]): Current population function/fitness values.
best_x (numpy.ndarray): Global best individual.
best_fitness (float): Global best individual fitness/function value.
**params (Dict[str, Any]): Additional arguments.
Returns:
Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray, float, Dict[str, Any]]:
1. New population.
2. New population fitness/function values.
3. New global best solution.
4. New global best fitness/objective value.
5. Additional arguments:
* ngh (float): A small value used for patches.
"""
ngh = params.pop('ngh')
for ies in range(self.e):
best_bee_pos = None
best_bee_cost = np.inf
for ieb in range(self.nep):
new_bee_pos, new_bee_cost = self.bee_dance(population[ies, :], task, ngh)
if new_bee_cost < best_bee_cost:
best_bee_cost = new_bee_cost
best_bee_pos = new_bee_pos
if best_bee_cost < population_fitness[ies]:
population[ies, :] = best_bee_pos
population_fitness[ies] = best_bee_cost
for ies in range(self.e, self.m):
best_bee_pos = None
best_bee_cost = np.inf
for ieb in range(self.nsp):
new_bee_pos, new_bee_cost = self.bee_dance(population[ies, :], task, ngh)
if new_bee_cost < best_bee_cost:
best_bee_cost = new_bee_cost
best_bee_pos = new_bee_pos
if best_bee_cost < population_fitness[ies]:
population[ies, :] = best_bee_pos
population_fitness[ies] = best_bee_cost
for ies in range(self.m, self.population_size):
population[ies, :] = self.uniform(task.lower, task.upper, task.dimension)
population_fitness[ies] = task.eval(population[ies, :])
sorted_indices = np.argsort(population_fitness)
population_fitness = population_fitness[sorted_indices]
population = population[sorted_indices, :]
ngh = ngh * 0.95
return population, population_fitness, population[0].copy(), population_fitness[0], {'ngh': ngh}
