# encoding=utf8
import logging
from NiaPy.algorithms.algorithm import Individual
from NiaPy.algorithms.basic.de import DifferentialEvolution, CrossBest1, CrossRand1, CrossCurr2Best1, CrossBest2, CrossCurr2Rand1, proportional, multiMutations, DynNpDifferentialEvolution
from NiaPy.util.utility import objects2array
logging.basicConfig()
logger = logging.getLogger('NiaPy.algorithms.modified')
logger.setLevel('INFO')
__all__ = [
'SelfAdaptiveDifferentialEvolution',
'DynNpSelfAdaptiveDifferentialEvolutionAlgorithm',
'AgingSelfAdaptiveDifferentialEvolution',
'MultiStrategySelfAdaptiveDifferentialEvolution',
'DynNpMultiStrategySelfAdaptiveDifferentialEvolution'
]
class SolutionjDE(Individual):
r"""Individual for jDE algorithm.
Attributes:
F (float): Scale factor.
CR (float): Crossover probability.
See Also:
:class:`NiaPy.algorithms.Individual`
"""
def __init__(self, F=2, CR=0.5, **kwargs):
r"""Initialize SolutionjDE.
Attributes:
F (float): Scale factor.
CR (float): Crossover probability.
See Also:
:func:`NiaPy.algorithm.Individual.__init__`
"""
Individual.__init__(self, **kwargs)
self.F, self.CR = F, CR
[docs]class SelfAdaptiveDifferentialEvolution(DifferentialEvolution):
r"""Implementation of Self-adaptive differential evolution algorithm.
Algorithm:
Self-adaptive differential evolution algorithm
Date:
2018
Author:
Uros Mlakar and Klemen Berkovič
License:
MIT
Reference paper:
Brest, J., Greiner, S., Boskovic, B., Mernik, M., Zumer, V. Self-adapting control parameters in differential evolution: A comparative study on numerical benchmark problems. IEEE transactions on evolutionary computation, 10(6), 646-657, 2006.
Attributes:
Name (List[str]): List of strings representing algorithm name
F_l (float): Scaling factor lower limit.
F_u (float): Scaling factor upper limit.
Tao1 (float): Change rate for F parameter update.
Tao2 (float): Change rate for CR parameter update.
See Also:
* :class:`NiaPy.algorithms.basic.DifferentialEvolution`
"""
Name = ['SelfAdaptiveDifferentialEvolution', 'jDE']
[docs] @staticmethod
def algorithmInfo():
r"""Get algorithm information.
Returns:
str: Algorithm information.
See Also:
* :func:`NiaPy.algorithms.Algorithm.algorithmInfo`
"""
return r"""Brest, J., Greiner, S., Boskovic, B., Mernik, M., Zumer, V. Self-adapting control parameters in differential evolution: A comparative study on numerical benchmark problems. IEEE transactions on evolutionary computation, 10(6), 646-657, 2006."""
[docs] @staticmethod
def typeParameters():
r"""Get dictionary with functions for checking values of parameters.
Returns:
Dict[str, Callable]:
* F_l (Callable[[Union[float, int]], bool])
* F_u (Callable[[Union[float, int]], bool])
* Tao1 (Callable[[Union[float, int]], bool])
* Tao2 (Callable[[Union[float, int]], bool])
See Also:
* :func:`NiaPy.algorithms.basic.DifferentialEvolution.typeParameters`
"""
d = DifferentialEvolution.typeParameters()
d['F_l'] = lambda x: isinstance(x, (float, int)) and x > 0
d['F_u'] = lambda x: isinstance(x, (float, int)) and x > 0
d['Tao1'] = lambda x: isinstance(x, (float, int)) and 0 <= x <= 1
d['Tao2'] = lambda x: isinstance(x, (float, int)) and 0 <= x <= 1
return d
[docs] def setParameters(self, F_l=0.0, F_u=1.0, Tao1=0.4, Tao2=0.2, **ukwargs):
r"""Set the parameters of an algorithm.
Arguments:
F_l (Optional[float]): Scaling factor lower limit.
F_u (Optional[float]): Scaling factor upper limit.
Tao1 (Optional[float]): Change rate for F parameter update.
Tao2 (Optional[float]): Change rate for CR parameter update.
See Also:
* :func:`NiaPy.algorithms.basic.DifferentialEvolution.setParameters`
"""
DifferentialEvolution.setParameters(self, itype=ukwargs.pop('itype', SolutionjDE), **ukwargs)
self.F_l, self.F_u, self.Tao1, self.Tao2 = F_l, F_u, Tao1, Tao2
[docs] def getParameters(self):
r"""TODO.
Returns:
Dict[str, Any]: TODO.
"""
d = DifferentialEvolution.getParameters(self)
d.update({
'F_l': self.F_l,
'F_u': self.F_u,
'Tao1': self.Tao1,
'Tao2': self.Tao2
})
return d
[docs] def AdaptiveGen(self, x):
r"""Adaptive update scale factor in crossover probability.
Args:
x (Individual): Individual to apply function on.
Returns:
Individual: New individual with new parameters
"""
f = self.F_l + self.rand() * (self.F_u - self.F_l) if self.rand() < self.Tao1 else x.F
cr = self.rand() if self.rand() < self.Tao2 else x.CR
return self.itype(x=x.x, F=f, CR=cr, e=False)
[docs] def evolve(self, pop, xb, task, **ukwargs):
r"""Evolve current population.
Args:
pop (numpy.ndarray[Individual]): Current population.
xb (Individual): Global best individual.
task (Task): Optimization task.
ukwargs (Dict[str, Any]): Additional arguments.
Returns:
numpy.ndarray: New population.
"""
npop = objects2array([self.AdaptiveGen(e) for e in pop])
for i, e in enumerate(npop): npop[i].x = self.CrossMutt(npop, i, xb, e.F, e.CR, rnd=self.Rand)
for e in npop: e.evaluate(task, rnd=self.rand)
return npop
class AgingIndividualJDE(SolutionjDE):
r"""Individual with age.
Attributes:
age (int): Age of individual.
See Also:
* :func:`NiaPy.algorithms.modified.SolutionjDE`
"""
def __init__(self, **kwargs):
r"""Initialize aging individual for jDE algorithm.
Args:
**kwargs (Dict[str, Any]): Additional arguments.
See Also:
* :func:`NiaPy.algorithms.modified.SolutionjDE.__init__`
"""
SolutionjDE.__init__(self, **kwargs)
self.age = 0
[docs]class AgingSelfAdaptiveDifferentialEvolution(SelfAdaptiveDifferentialEvolution):
r"""Implementation of Dynamic population size with aging self-adaptive differential evolution algorithm.
Algorithm:
Dynamic population size with aging self-adaptive self adaptive differential evolution algorithm
Date:
2018
Author:
Jan Popič and Klemen Berkovič
License:
MIT
Reference URL:
https://link.springer.com/article/10.1007/s10489-007-0091-x
Reference paper:
Brest, Janez, and Mirjam Sepesy Maučec. Population size reduction for the differential evolution algorithm. Applied Intelligence 29.3 (2008): 228-247.
Attributes:
Name (List[str]): List of strings representing algorithm name.
"""
Name = ['AgingSelfAdaptiveDifferentialEvolution', 'ANpjDE']
[docs] @staticmethod
def algorithmInfo():
r"""Get basic information about the algorithm.
Returns:
str: Basic information.
See Also:
* :func:`NiaPy.algorithms.Algorithm.algorithmInfo`
"""
return r"""Brest, Janez, and Mirjam Sepesy Maučec. Population size reduction for the differential evolution algorithm. Applied Intelligence 29.3 (2008): 228-247."""
[docs] @staticmethod
def typeParameters():
d = SelfAdaptiveDifferentialEvolution.typeParameters()
# TODO
return d
[docs] def setParameters(self, LT_min=1, LT_max=7, age=proportional, **ukwargs):
r"""Set core parameters of AgingSelfAdaptiveDifferentialEvolution algorithm.
Args:
LT_min (Optional[int]): Minimum age.
LT_max (Optional[int]): Maximum age.
age (Optional[Callable[[], int]]): Function for calculating age of individual.
**ukwargs (Dict[str, Any]): Additional arguments.
See Also:
* :func:`SelfAdaptiveDifferentialEvolution.setParameters`
"""
SelfAdaptiveDifferentialEvolution.setParameters(self, **ukwargs)
self.LT_min, self.LT_max, self.age = LT_min, LT_max, age
self.mu = abs(self.LT_max - self.LT_min) / 2
[docs]class DynNpSelfAdaptiveDifferentialEvolutionAlgorithm(SelfAdaptiveDifferentialEvolution, DynNpDifferentialEvolution):
r"""Implementation of Dynamic population size self-adaptive differential evolution algorithm.
Algorithm:
Dynamic population size self-adaptive differential evolution algorithm
Date:
2018
Author:
Jan Popič and Klemen Berkovič
License:
MIT
Reference URL:
https://link.springer.com/article/10.1007/s10489-007-0091-x
Reference paper:
Brest, Janez, and Mirjam Sepesy Maučec. Population size reduction for the differential evolution algorithm. Applied Intelligence 29.3 (2008): 228-247.
Attributes:
Name (List[str]): List of strings representing algorithm name.
rp (int): Small non-negative number which is added to value of generations.
pmax (int): Number of population reductions.
See Also:
* :class:`NiaPy.algorithms.modified.SelfAdaptiveDifferentialEvolution`
"""
Name = ['DynNpSelfAdaptiveDifferentialEvolutionAlgorithm', 'dynNPjDE']
[docs] @staticmethod
def algorithmInfo():
r"""Get basic information about the algorithm.
Returns:
str: Basic information.
See Also:
* :func:`NiaPy.algorithms.Algorithm.algorithmInfo`
"""
return r"""Brest, Janez, and Mirjam Sepesy Maučec. Population size reduction for the differential evolution algorithm. Applied Intelligence 29.3 (2008): 228-247."""
[docs] @staticmethod
def typeParameters():
r"""Get basic information of algorithm.
Returns:
str: Basic information of algorithm.
See Also:
* :func:`NiaPy.algorithms.Algorithm.algorithmInfo`
"""
d = SelfAdaptiveDifferentialEvolution.typeParameters()
d['rp'] = lambda x: isinstance(x, (float, int)) and x > 0
d['pmax'] = lambda x: isinstance(x, int) and x > 0
return d
[docs] def setParameters(self, rp=0, pmax=10, **ukwargs):
r"""Set the parameters of an algorithm.
Arguments:
rp (Optional[int]): Small non-negative number which is added to value of genp (if it's not divisible).
pmax (Optional[int]): Number of population reductions.
See Also:
* :func:`NiaPy.algorithms.modified.SelfAdaptiveDifferentialEvolution.setParameters`
"""
DynNpDifferentialEvolution.setParameters(self, rp=rp, pmax=pmax, **ukwargs)
SelfAdaptiveDifferentialEvolution.setParameters(self, **ukwargs)
[docs] def postSelection(self, pop, task, **kwargs):
r"""Post selection operator.
Args:
pop (numpy.ndarray[Individual]): Current population.
task (Task): Optimization task.
Returns:
numpy.ndarray[Individual]: New population.
"""
return DynNpDifferentialEvolution.postSelection(self, pop, task, **kwargs)
[docs]class MultiStrategySelfAdaptiveDifferentialEvolution(SelfAdaptiveDifferentialEvolution):
r"""Implementation of self-adaptive differential evolution algorithm with multiple mutation strategys.
Algorithm:
Self-adaptive differential evolution algorithm with multiple mutation strategys
Date:
2018
Author:
Klemen Berkovič
License:
MIT
Attributes:
Name (List[str]): List of strings representing algorithm name
See Also:
* :class:`NiaPy.algorithms.modified.SelfAdaptiveDifferentialEvolution`
"""
Name = ['MultiStrategySelfAdaptiveDifferentialEvolution', 'MsjDE']
[docs] def setParameters(self, strategies=(CrossCurr2Rand1, CrossCurr2Best1, CrossRand1, CrossBest1, CrossBest2), **kwargs):
r"""Set core parameters of MultiStrategySelfAdaptiveDifferentialEvolution algorithm.
Args:
strategys (Optional[Iterable[Callable]]): Mutations strategies to use in algorithm.
**kwargs:
See Also:
* :func:`NiaPy.algorithms.modified.SelfAdaptiveDifferentialEvolution.setParameters`
"""
SelfAdaptiveDifferentialEvolution.setParameters(self, CrossMutt=kwargs.pop('CrossMutt', multiMutations), **kwargs)
self.strategies = strategies
[docs] def evolve(self, pop, xb, task, **kwargs):
r"""Evolve population with the help multiple mutation strategies.
Args:
pop (numpy.ndarray[Individual]): Current population.
xb (Individual): Current best individual.
task (Task): Optimization task.
**kwargs (Dict[str, Any]): Additional arguments.
Returns:
numpy.ndarray[Individual]: New population of individuals.
"""
return objects2array([self.CrossMutt(pop, i, xb, self.F, self.CR, self.Rand, task, self.itype, self.strategies) for i in range(len(pop))])
[docs]class DynNpMultiStrategySelfAdaptiveDifferentialEvolution(MultiStrategySelfAdaptiveDifferentialEvolution, DynNpSelfAdaptiveDifferentialEvolutionAlgorithm):
r"""Implementation of Dynamic population size self-adaptive differential evolution algorithm with multiple mutation strategies.
Algorithm:
Dynamic population size self-adaptive differential evolution algorithm with 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.MultiStrategySelfAdaptiveDifferentialEvolution`
* :class:`NiaPy.algorithms.modified.DynNpSelfAdaptiveDifferentialEvolutionAlgorithm`
"""
Name = ['DynNpMultiStrategySelfAdaptiveDifferentialEvolution', 'dynNpMsjDE']
[docs] def setParameters(self, pmax=10, rp=5, **kwargs):
r"""Set core parameters for algorithm instance.
Args:
pmax (Optional[int]):
rp (Optional[int]):
**kwargs (Dict[str, Any]):
See Also:
* :func:`NiaPy.algorithms.modified.MultiStrategySelfAdaptiveDifferentialEvolution.setParameters`
"""
MultiStrategySelfAdaptiveDifferentialEvolution.setParameters(self, **kwargs)
self.pmax, self.rp = pmax, rp
[docs] def postSelection(self, pop, task, **kwargs):
return DynNpSelfAdaptiveDifferentialEvolutionAlgorithm.postSelection(self, pop, task)
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