# encoding=utf8
import logging
from numpy import argmax, log, exp, full
from NiaPy.algorithms.algorithm import Algorithm
logging.basicConfig()
logger = logging.getLogger("NiaPy.algorithms.basic")
logger.setLevel("INFO")
__all__ = ["HarmonySearch", "HarmonySearchV1"]
[docs]class HarmonySearch(Algorithm):
r"""Implementation of harmony search algorithm.
Algorithm:
Harmony Search Algorithm
Date:
2018
Authors:
Klemen Berkovič
License:
MIT
Reference URL:
https://link.springer.com/chapter/10.1007/978-3-642-00185-7_1
Reference paper:
Geem, Z. W., Kim, J. H., & Loganathan, G. V. (2001). A new heuristic optimization algorithm: harmony search. Simulation, 76(2), 60-68.
Attributes:
Name (List[str]): List of strings representing algorithm names
r_accept (float): Probability of accepting new bandwidth into harmony.
r_pa (float): Probability of accepting random bandwidth into harmony.
b_range (float): Range of bandwidth.
See Also:
* :class:`NiaPy.algorithms.algorithm.Algorithm`
"""
Name = ["HarmonySearch", "HS"]
[docs] @staticmethod
def algorithmInfo():
r"""Get basic information about the algorithm.
Returns:
str: Basic information.
"""
return r"""Geem, Z. W., Kim, J. H., & Loganathan, G. V. (2001). A new heuristic optimization algorithm: harmony search. Simulation, 76(2), 60-68."""
[docs] @staticmethod
def typeParameters():
r"""Get dictionary with functions for checking values of parameters.
Returns:
Dict[str, Callable]:
* HMS (Callable[[int], bool])
* r_accept (Callable[[float], bool])
* r_pa (Callable[[float], bool])
* b_range (Callable[[float], bool])
"""
return {
"HMS": lambda x: isinstance(x, int) and x > 0,
"r_accept": lambda x: isinstance(x, float) and 0 < x < 1,
"r_pa": lambda x: isinstance(x, float) and 0 < x < 1,
"b_range": lambda x: isinstance(x, (int, float)) and x > 0
}
[docs] def setParameters(self, HMS=30, r_accept=0.7, r_pa=0.35, b_range=1.42, **ukwargs):
r"""Set the arguments of the algorithm.
Arguments:
HMS (Optional[int]): Number of harmony in the memory.
r_accept (Optional[float]): Probability of accepting new bandwidth to harmony.
r_pa (Optional[float]): Probability of accepting random bandwidth into harmony.
b_range (Optional[float]): Bandwidth range.
See Also:
* :func:`NiaPy.algorithms.algorithm.Algorithm.setParameters`
"""
ukwargs.pop('NP', None)
Algorithm.setParameters(self, NP=HMS, **ukwargs)
self.r_accept, self.r_pa, self.b_range = r_accept, r_pa, b_range
[docs] def getParameters(self):
d = Algorithm.getParameters(self)
d.pop('NP', None)
d.update({
'HMS': self.NP,
'r_accept': self.r_accept,
'r_pa': self.r_pa,
'b_range': self.b_range
})
return d
[docs] def bw(self, task):
r"""Get bandwidth.
Args:
task (Task): Optimization task.
Returns:
float: Bandwidth.
"""
return self.uniform(-1, 1) * self.b_range
[docs] def adjustment(self, x, task):
r"""Adjust value based on bandwidth.
Args:
x (Union[int, float]): Current position.
task (Task): Optimization task.
Returns:
float: New position.
"""
return x + self.bw(task)
[docs] def improvize(self, HM, task):
r"""Create new individual.
Args:
HM (numpy.ndarray): Current population.
task (Task): Optimization task.
Returns:
numpy.ndarray: New individual.
"""
H = full(task.D, .0)
for i in range(task.D):
r, j = self.rand(), self.randint(self.NP)
H[i] = HM[j, i] if r > self.r_accept else self.adjustment(HM[j, i], task) if r > self.r_pa else self.uniform(task.Lower[i], task.Upper[i])
return H
[docs] def initPopulation(self, task):
r"""Initialize first population.
Args:
task (Task): Optimization task.
Returns:
Tuple[numpy.ndarray, numpy.ndarray[float], Dict[str, Any]]:
1. New harmony/population.
2. New population fitness/function values.
3. Additional parameters.
See Also:
* :func:`NiaPy.algorithms.algorithm.Algorithm.initPopulation`
"""
return Algorithm.initPopulation(self, task)
[docs] def runIteration(self, task, HM, HM_f, xb, fxb, **dparams):
r"""Core function of HarmonySearch algorithm.
Args:
task (Task): Optimization task.
HM (numpy.ndarray): Current population.
HM_f (numpy.ndarray): Current populations function/fitness values.
xb (numpy.ndarray): Global best individual.
fxb (float): Global best fitness/function value.
**dparams (Dict[str, Any]): Additional arguments.
Returns:
Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray, float, Dict[str, Any]]:
1. New harmony/population.
2. New populations function/fitness values.
3. New global best solution
4. New global best solution fitness/objective value
5. Additional arguments.
"""
H = self.improvize(HM, task)
H_f = task.eval(task.repair(H, self.Rand))
iw = argmax(HM_f)
if H_f <= HM_f[iw]: HM[iw], HM_f[iw] = H, H_f
xb, fxb = self.getBest(H, H_f, xb, fxb)
return HM, HM_f, xb, fxb, {}
[docs]class HarmonySearchV1(HarmonySearch):
r"""Implementation of harmony search algorithm.
Algorithm:
Harmony Search Algorithm
Date:
2018
Authors:
Klemen Berkovič
License:
MIT
Reference URL:
https://link.springer.com/chapter/10.1007/978-3-642-00185-7_1
Reference paper:
Yang, Xin-She. "Harmony search as a metaheuristic algorithm." Music-inspired harmony search algorithm. Springer, Berlin, Heidelberg, 2009. 1-14.
Attributes:
Name (List[str]): List of strings representing algorithm name.
bw_min (float): Minimal bandwidth.
bw_max (float): Maximal bandwidth.
See Also:
* :class:`NiaPy.algorithms.basic.hs.HarmonySearch`
"""
Name = ["HarmonySearchV1", "HSv1"]
[docs] @staticmethod
def algorithmInfo():
r"""Get basic information about algorihtm.
Returns:
str: Basic information.
"""
return r"""Yang, Xin-She. "Harmony search as a metaheuristic algorithm." Music-inspired harmony search algorithm. Springer, Berlin, Heidelberg, 2009. 1-14."""
[docs] @staticmethod
def typeParameters():
r"""Get dictionary with functions for checking values of parameters.
Returns:
Dict[str, Callable]: Function for testing correctness of parameters.
See Also:
* :func:`NiaPy.algorithms.basic.HarmonySearch.typeParameters`
"""
d = HarmonySearch.typeParameters()
del d["b_range"]
d.update({
"dw_min": lambda x: isinstance(x, (float, int)) and x >= 1,
"dw_max": lambda x: isinstance(x, (float, int)) and x >= 1
})
return d
[docs] def setParameters(self, bw_min=1, bw_max=2, **kwargs):
r"""Set the parameters of the algorithm.
Arguments:
bw_min (Optional[float]): Minimal bandwidth
bw_max (Optional[float]): Maximal bandwidth
kwargs (Dict[str, Any]): Additional arguments.
See Also:
* :func:`NiaPy.algorithms.basic.hs.HarmonySearch.setParameters`
"""
HarmonySearch.setParameters(self, **kwargs)
self.bw_min, self.bw_max = bw_min, bw_max
[docs] def getParameters(self):
d = HarmonySearch.getParameters(self)
d.update({
'bw_min': self.bw_min,
'bw_max': self.bw_max
})
return d
[docs] def bw(self, task):
r"""Get new bandwidth.
Args:
task (Task): Optimization task.
Returns:
float: New bandwidth.
"""
return self.bw_min * exp(log(self.bw_min / self.bw_max) * task.Iters / task.nGEN)
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