# encoding=utf8
import logging
import numpy as np
from niapy.algorithms.algorithm import Individual
from niapy.algorithms.basic.de import MultiStrategyDifferentialEvolution, DynNpDifferentialEvolution, DifferentialEvolution
from niapy.algorithms.other.mts import mts_ls1v1, mts_ls2, mts_ls3v1, MultipleTrajectorySearch
logging.basicConfig()
logger = logging.getLogger('niapy.algorithms.modified')
logger.setLevel('INFO')
__all__ = ['DifferentialEvolutionMTS', 'DifferentialEvolutionMTSv1', 'DynNpDifferentialEvolutionMTS',
'DynNpDifferentialEvolutionMTSv1', 'MultiStrategyDifferentialEvolutionMTS',
'MultiStrategyDifferentialEvolutionMTSv1', 'DynNpMultiStrategyDifferentialEvolutionMTS',
'DynNpMultiStrategyDifferentialEvolutionMTSv1']
class MtsIndividual(Individual):
r"""Individual for MTS local searches.
Attributes:
search_range (numpy.ndarray): Search range.
grade (int): Grade of individual.
enable (bool): If enabled.
improved (bool): If improved.
See Also:
:class:`niapy.algorithms.algorithm.Individual`
"""
def __init__(self, search_range=None, grade=0, enable=True, improved=False, task=None, **kwargs):
r"""Initialize the individual.
Args:
search_range (numpy.ndarray): Search range.
grade (Optional[int]): Grade of individual.
enable (Optional[bool]): If enabled individual.
improved (Optional[bool]): If individual improved.
See Also:
:func:`niapy.algorithms.algorithm.Individual.__init__`
"""
super().__init__(task=task, **kwargs)
self.grade, self.enable, self.improved = grade, enable, improved
if search_range is None and task is not None:
self.search_range = task.range / 4
else:
self.search_range = search_range
[docs]class DifferentialEvolutionMTS(DifferentialEvolution, MultipleTrajectorySearch):
r"""Implementation of Differential Evolution with MTS local searches.
Algorithm:
Differential Evolution with MTS local searches
Date:
2018
Author:
Klemen Berkovič
License:
MIT
Attributes:
Name (List[str]): List of strings representing algorithm names.
See Also:
* :class:`niapy.algorithms.basic.de.DifferentialEvolution`
* :class:`niapy.algorithms.other.mts.MultipleTrajectorySearch`
"""
Name = ['DifferentialEvolutionMTS', 'DEMTS']
[docs] @staticmethod
def info():
r"""Get basic information about the algorithm.
Returns:
str: Basic information.
See Also:
* :func:`niapy.algorithms.Algorithm.info`
"""
return r"""TODO"""
[docs] def __init__(self, population_size=40, *args, **kwargs):
"""Initialize DifferentialEvolutionMTS."""
super().__init__(population_size, individual_type=kwargs.pop('individual_type', MtsIndividual), *args, **kwargs)
[docs] def set_parameters(self, **kwargs):
r"""Set the algorithm parameters.
See Also:
:func:`niapy.algorithms.basic.de.DifferentialEvolution.set_parameters`
"""
MultipleTrajectorySearch.set_parameters(self, **kwargs)
DifferentialEvolution.set_parameters(self, individual_type=kwargs.pop('individual_type', MtsIndividual),
**kwargs)
[docs] def get_parameters(self):
"""Get algorithm parameters."""
d = DifferentialEvolution.get_parameters(self)
d.update(MultipleTrajectorySearch.get_parameters(self))
return d
[docs] def post_selection(self, population, task, xb, fxb, **kwargs):
r"""Post selection operator.
Args:
population (numpy.ndarray): Current population.
task (Task): Optimization task.
xb (numpy.ndarray): Global best individual.
fxb (float): Global best fitness.
Returns:
Tuple[numpy.ndarray, numpy.ndarray, float]: New population.
"""
for x in population:
if not x.enable:
continue
x.enable, x.grades = False, 0
x.x, x.f, xb, fxb, k = self.grading_run(x.x, x.f, xb, fxb, x.improved, x.search_range, task)
x.x, x.f, xb, fxb, x.improved, x.search_range, x.grades = self.run_local_search(k, x.x, x.f, xb, fxb, x.improved, x.search_range, 0, task)
for i in population[np.argsort([x.grade for x in population])[:self.num_enabled:]]:
i.enable = True
return population, xb, fxb
[docs]class DifferentialEvolutionMTSv1(DifferentialEvolutionMTS):
r"""Implementation of Differential Evolution with MTSv1 local searches.
Algorithm:
Differential Evolution with MTSv1 local searches
Date:
2018
Author:
Klemen Berkovič
License:
MIT
Attributes:
Name (List[str]): List of strings representing algorithm name.
See Also:
:class:`niapy.algorithms.modified.DifferentialEvolutionMTS`
"""
Name = ['DifferentialEvolutionMTSv1', 'DEMTSv1']
[docs] @staticmethod
def info():
r"""Get basic information about the algorithm.
Returns:
str: Basic information.
See Also:
* :func:`niapy.algorithms.Algorithm.info`
"""
return r"""TODO"""
[docs] def __init__(self, *args, **kwargs):
"""Initialize DifferentialEvolutionMTSv1."""
super().__init__(local_searches=(mts_ls1v1, mts_ls2, mts_ls3v1), *args, **kwargs)
[docs] def set_parameters(self, **kwargs):
r"""Set core parameters of DifferentialEvolutionMTSv1 algorithm.
See Also:
:func:`niapy.algorithms.modified.DifferentialEvolutionMTS.set_parameters`
"""
super().set_parameters(local_searches=(mts_ls1v1, mts_ls2, mts_ls3v1), **kwargs)
[docs]class DynNpDifferentialEvolutionMTS(DifferentialEvolutionMTS, DynNpDifferentialEvolution):
r"""Implementation of Differential Evolution with MTS local searches dynamic and population size.
Algorithm:
Differential Evolution with MTS local searches and dynamic population size
Date:
2018
Author:
Klemen Berkovič
License:
MIT
Attributes:
Name (List[str]): List of strings representing algorithm name
See Also:
* :class:`niapy.algorithms.modified.DifferentialEvolutionMTS`
* :class:`niapy.algorithms.basic.de.DynNpDifferentialEvolution`
"""
Name = ['DynNpDifferentialEvolutionMTS', 'dynNpDEMTS']
[docs] @staticmethod
def info():
r"""Get basic information about the algorithm.
Returns:
str: Basic information.
See Also:
* :func:`niapy.algorithms.Algorithm.info`
"""
return r"""TODO"""
[docs] def __init__(self, *args, **kwargs):
"""Initialize DynNpDifferentialEvolutionMTS."""
super().__init__(*args, **kwargs)
[docs] def set_parameters(self, p_max=10, rp=3, **kwargs):
r"""Set core parameters or DynNpDifferentialEvolutionMTS algorithm.
Args:
p_max (Optional[int]):
rp (Optional[float]):
See Also:
* :func:`niapy.algorithms.modified.hde.DifferentialEvolutionMTS.set_parameters`
* :func`niapy.algorithms.basic.de.DynNpDifferentialEvolution.set_parameters`
"""
DynNpDifferentialEvolution.set_parameters(self, p_max=p_max, rp=rp, **kwargs)
DifferentialEvolutionMTS.set_parameters(self, **kwargs)
[docs] def get_parameters(self):
r"""Get parameters of the algorithm.
Returns:
Dict[str, Any]: Algorithm parameters.
"""
params = DynNpDifferentialEvolution.get_parameters(self)
params.update(DifferentialEvolutionMTS.get_parameters(self))
return params
[docs] def post_selection(self, population, task, xb, fxb, **kwargs):
new_x, xb, fxb = DynNpDifferentialEvolution.post_selection(self, population, task, xb, fxb)
new_x, xb, fxb = DifferentialEvolutionMTS.post_selection(self, new_x, task, xb, fxb)
return new_x, xb, fxb
[docs]class DynNpDifferentialEvolutionMTSv1(DynNpDifferentialEvolutionMTS):
r"""Implementation of Differential Evolution with MTSv1 local searches and dynamic population size.
Algorithm:
Differential Evolution with MTSv1 local searches and dynamic population size
Date:
2018
Author:
Klemen Berkovič
License:
MIT
Attributes:
Name (List[str]): List of strings representing algorithm name.
See Also:
:class:`niapy.algorithms.modified.hde.DifferentialEvolutionMTS`
"""
Name = ['DynNpDifferentialEvolutionMTSv1', 'dynNpDEMTSv1']
[docs] @staticmethod
def info():
r"""Get basic information about the algorithm.
Returns:
str: Basic information.
See Also:
* :func:`niapy.algorithms.Algorithm.info`
"""
return r"""TODO"""
[docs] def __init__(self, *args, **kwargs):
"""Initialize DynNpDifferentialEvolutionMTSv1."""
super().__init__(local_searches=(mts_ls1v1, mts_ls2, mts_ls3v1), *args, **kwargs)
[docs] def set_parameters(self, **kwargs):
r"""Set core arguments of DynNpDifferentialEvolutionMTSv1 algorithm.
See Also:
:func:`niapy.algorithms.modified.hde.DifferentialEvolutionMTS.set_parameters`
"""
DynNpDifferentialEvolutionMTS.set_parameters(self, local_searches=(mts_ls1v1, mts_ls2, mts_ls3v1), **kwargs)
[docs]class MultiStrategyDifferentialEvolutionMTS(DifferentialEvolutionMTS, MultiStrategyDifferentialEvolution):
r"""Implementation of Differential Evolution with MTS local searches and multiple mutation strategies.
Algorithm:
Differential Evolution with MTS local searches and multiple mutation strategies
Date:
2018
Author:
Klemen Berkovič
License:
MIT
Attributes:
Name (List[str]): List of strings representing algorithm name.
See Also:
* :class:`niapy.algorithms.modified.hde.DifferentialEvolutionMTS`
* :class:`niapy.algorithms.basic.de.MultiStrategyDifferentialEvolution`
"""
Name = ['MultiStrategyDifferentialEvolutionMTS', 'MSDEMTS']
[docs] @staticmethod
def info():
r"""Get basic information about the algorithm.
Returns:
str: Basic information.
See Also:
* :func:`niapy.algorithms.Algorithm.info`
"""
return r"""TODO"""
[docs] def __init__(self, *args, **kwargs):
"""Initialize MultiStrategyDifferentialEvolutionMTS."""
super().__init__(individual_type=kwargs.pop('individual_type', MtsIndividual), *args, **kwargs)
[docs] def set_parameters(self, **kwargs):
r"""Set algorithm parameters.
See Also:
* :func:`niapy.algorithms.modified.DifferentialEvolutionMTS.set_parameters`
* :func:`niapy.algorithms.basic.MultiStrategyDifferentialEvolution.set_parameters`
"""
DifferentialEvolutionMTS.set_parameters(self, **kwargs)
MultiStrategyDifferentialEvolution.set_parameters(self, individual_type=kwargs.pop('individual_type', MtsIndividual), **kwargs)
[docs] def get_parameters(self):
r"""Get parameters of the algorithm.
Returns:
Dict[str, Any]: Algorithm parameters.
"""
params = DifferentialEvolutionMTS.get_parameters(self)
params.update(MultiStrategyDifferentialEvolution.get_parameters(self))
return params
[docs] def evolve(self, pop, xb, task, **kwargs):
r"""Evolve population.
Args:
pop (numpy.ndarray[Individual]): Current population of individuals.
xb (numpy.ndarray): Global best individual.
task (Task): Optimization task.
Returns:
numpy.ndarray[Individual]: Evolved population.
"""
return MultiStrategyDifferentialEvolution.evolve(self, pop, xb, task, **kwargs)
[docs]class MultiStrategyDifferentialEvolutionMTSv1(MultiStrategyDifferentialEvolutionMTS):
r"""Implementation of Differential Evolution with MTSv1 local searches and multiple mutation strategies.
Algorithm:
Differential Evolution with MTSv1 local searches and multiple mutation strategies
Date:
2018
Author:
Klemen Berkovič
License:
MIT
Attributes:
Name (List[str]): List of stings representing algorithm name.
See Also:
* :class:`niapy.algorithms.modified.MultiStrategyDifferentialEvolutionMTS`
"""
Name = ['MultiStrategyDifferentialEvolutionMTSv1', 'MSDEMTSv1']
[docs] @staticmethod
def info():
r"""Get basic information about the algorithm.
Returns:
str: Basic information.
See Also:
* :func:`niapy.algorithms.Algorithm.info`
"""
return r"""TODO"""
[docs] def __init__(self, *args, **kwargs):
"""Initialize MultiStrategyDifferentialEvolutionMTSv1."""
super().__init__(local_searches=(mts_ls1v1, mts_ls2, mts_ls3v1), *args, **kwargs)
[docs] def set_parameters(self, **kwargs):
r"""Set core parameters of MultiStrategyDifferentialEvolutionMTSv1 algorithm.
See Also:
* :func:`niapy.algorithms.modified.MultiStrategyDifferentialEvolutionMTS.set_parameters`
"""
MultiStrategyDifferentialEvolutionMTS.set_parameters(self, local_searches=(mts_ls1v1, mts_ls2, mts_ls3v1), **kwargs)
[docs]class DynNpMultiStrategyDifferentialEvolutionMTS(MultiStrategyDifferentialEvolutionMTS, DynNpDifferentialEvolutionMTS):
r"""Implementation of Differential Evolution with MTS local searches, multiple mutation strategies and dynamic population size.
Algorithm:
Differential Evolution with MTS local searches, multiple mutation strategies and dynamic population size
Date:
2018
Author:
Klemen Berkovič
License:
MIT
Attributes:
Name (List[str]): List of strings representing algorithm name
See Also:
* :class:`niapy.algorithms.modified.MultiStrategyDifferentialEvolutionMTS`
* :class:`niapy.algorithms.modified.DynNpDifferentialEvolutionMTS`
"""
Name = ['DynNpMultiStrategyDifferentialEvolutionMTS', 'dynNpMSDEMTS']
[docs] @staticmethod
def info():
r"""Get basic information about the algorithm.
Returns:
str: Basic information.
See Also:
* :func:`niapy.algorithms.Algorithm.info`
"""
return r"""TODO"""
[docs] def __init__(self, *args, **kwargs):
"""Initialize DynNpMultiStrategyDifferentialEvolutionMTS."""
super().__init__(*args, **kwargs)
[docs] def set_parameters(self, **kwargs):
r"""Set core arguments of DynNpMultiStrategyDifferentialEvolutionMTS algorithm.
See Also:
* :func:`niapy.algorithms.modified.MultiStrategyDifferentialEvolutionMTS.set_parameters`
* :func:`niapy.algorithms.modified.DynNpDifferentialEvolutionMTS.set_parameters`
"""
DynNpDifferentialEvolutionMTS.set_parameters(self, **kwargs)
MultiStrategyDifferentialEvolutionMTS.set_parameters(self, **kwargs)
[docs] def get_parameters(self):
r"""Get parameters of the algorithm.
Returns:
Dict[str, Any]: Algorithm parameters.
"""
params = DynNpDifferentialEvolutionMTS.get_parameters(self)
params.update(MultiStrategyDifferentialEvolutionMTS.get_parameters(self))
return params
[docs]class DynNpMultiStrategyDifferentialEvolutionMTSv1(DynNpMultiStrategyDifferentialEvolutionMTS):
r"""Implementation of Differential Evolution with MTSv1 local searches, multiple mutation strategies and dynamic population size.
Algorithm:
Differential Evolution with MTSv1 local searches, multiple mutation strategies and dynamic population size
Date:
2018
Author:
Klemen Berkovič
License:
MIT
Attributes:
Name (List[str]): List of strings representing algorithm name.
See Also:
* :class:`niapy.algorithm.modified.DynNpMultiStrategyDifferentialEvolutionMTS`
"""
Name = ['DynNpMultiStrategyDifferentialEvolutionMTSv1', 'dynNpMSDEMTSv1']
[docs] @staticmethod
def info():
r"""Get basic information about the algorithm.
Returns:
str: Basic information.
See Also:
* :func:`niapy.algorithms.Algorithm.info`
"""
return r"""TODO"""
[docs] def __init__(self, *args, **kwargs):
"""Initialize DynNpMultiStrategyDifferentialEvolutionMTSv1."""
super().__init__(local_searches=(mts_ls1v1, mts_ls2, mts_ls3v1), *args, **kwargs)
[docs] def set_parameters(self, **kwargs):
r"""Set core parameters of DynNpMultiStrategyDifferentialEvolutionMTSv1 algorithm.
See Also:
* :func:`niapy.algorithm.modified.DynNpMultiStrategyDifferentialEvolutionMTS.set_parameters`
"""
DynNpMultiStrategyDifferentialEvolutionMTS.set_parameters(self, local_searches=(mts_ls1v1, mts_ls2, mts_ls3v1), **kwargs)
