def setParameters(self,
NP=25,
C1=2.0,
C2=2.0,
w=0.7,
vMin=-4,
vMax=4,
InitPopFunc=defaultNumPyInit,
binary=False,
**ukwargs):
r"""Set Particle Swarm Algorithm main parameters.
Args:
NP (Optional[int]): Population size
C1 (Optional[float]): Cognitive component
C2 (Optional[float]): Social component
w (Optional[float]): Inertial weight
vMin (Optional[float]): Mininal velocity
vMax (Optional[float]): Maximal velocity
InitPopFunc (Optional(func)): Init functions as in Algorithm Base Class
binary (Optional(bool)): If True, V is rounded and casted to int
**ukwargs: Additional arguments
See Also:
* :func:`NiaPy.algorithms.Algorithm.setParameters`
"""
Algorithm.setParameters(self, NP=NP, **ukwargs)
self.C1, self.C2, self.w, self.vMin, self.vMax = C1, C2, w, vMin, vMax
self.binary = binary
if ukwargs: logger.info('Unused arguments: %s' % (ukwargs))
def setParameters(self,
NP=25,
SI_init=3,
SI_final=10,
SV_init=3,
SV_final=13,
min_w=0.3,
w_scale=0.7,
**ukwargs):
r"""Set core arguments of FishSchoolSearch algorithm.
Arguments:
NP (Optional[int]): Number of fishes in school.
SI_init (Optional[int]): Length of initial individual step.
SI_final (Optional[int]): Length of final individual step.
SV_init (Optional[int]): Length of initial volatile step.
SV_final (Optional[int]): Length of final volatile step.
min_w (Optional[float]): Minimum weight of a fish.
w_scale (Optional[float]): Maximum weight of a fish.
See Also:
* :func:`NiaPy.algorithms.Algorithm.setParameters`
"""
Algorithm.setParameters(self, NP=NP)
self.step_individual_init = SI_init
self.step_individual_final = SI_final
self.step_volitive_init = SV_init
self.step_volitive_final = SV_final
self.min_w = min_w
self.w_scale = w_scale
def setParameters(self,
N=25,
phi=10,
Fa=0.5,
Fb=0.5,
Fd=0.3,
k=25,
P_Cr=0.5,
P_F=0.36,
SexualCrossover=SexualCrossoverSimple,
Brooding=BroodingSimple,
Distance=euclidean,
**ukwargs):
r"""Set the parameters of the algorithm.
Arguments:
N (int): population size for population initialization.
phi (int): TODO.
Fa (float): TODO.
Fb (float): Value $\in [0, 1]$ for Brooding size.
Fd (float): Value $\in [0, 1]$ for Depredation.
k (int): Trys for larvae setting.
SexualCrossover (Callable[[numpy.ndarray, float, Task, mtrand.RandomState, Dict[str, Any]], Tuple[numpy.ndarray, numpy.ndarray[float]]]): Crossover function.
P_Cr (float): Crossover rate $\in [0, 1]$.
Brooding (Callable[[numpy.ndarray, float, Task, mtrand.RandomState, Dict[str, Any]], Tuple[numpy.ndarray, numpy.ndarray[float]]]): Brooding function.
P_F (float): Crossover rate $\in [0, 1]$.
Distance (Callable[[numpy.ndarray, numpy.ndarray], float]): Funciton for calculating distance between corals.
See Also:
* :func:`NiaPy.algorithms.Algorithm.setParameters`
"""
Algorithm.setParameters(self, **ukwargs)
self.phi, self.Fa, self.Fb, self.Fd, self.k, self.P_Cr, self.P_F = phi, Fa, Fb, Fd, k, P_Cr, P_F
self.SexualCrossover, self.Brooding, self.Distance = SexualCrossover, Brooding, Distance
if ukwargs: logger.info('Unused arguments: %s' % (ukwargs))
def setParameters(self,
n=25,
l0=5,
nt=5,
rho=0.4,
gamma=0.6,
beta=0.08,
s=0.03,
Distance=euclidean,
**ukwargs):
r"""Set the arguments of an algorithm.
Arguments:
n (Optional[int]): Number of glowworms in population.
l0 (Optional[float]): Initial luciferin quantity for each glowworm.
nt (Optional[float]): --
rs (Optional]float]): Maximum sensing range.
rho (Optional[float]): Luciferin decay constant.
gamma (Optional[float]): Luciferin enhancement constant.
beta (Optional[float]): --
s (Optional[float]): --
Distance (Optional[Callable[[numpy.ndarray, numpy.ndarray], float]]]): Measure distance between two individuals.
"""
if "NP" in ukwargs:
n = ukwargs["NP"]
Algorithm.setParameters(self, **ukwargs)
self.n, self.l0, self.nt, self.rho, self.gamma, self.beta, self.s, self.Distance = n, l0, nt, rho, gamma, beta, s, Distance
if ukwargs: logger.info('Unused arguments: %s' % (ukwargs))
def setParameters(self,
NP=30,
MR=0.1,
C1=2.05,
SMP=3,
SPC=True,
CDC=0.85,
SRD=0.2,
vMax=1.9,
**ukwargs):
r"""Set the algorithm parameters.
Arguments:
NP (int): Number of individuals in population.
MR (float): Mixture ratio.
C1 (float): Constant in tracing mode.
SMP (int): Seeking memory pool.
SPC (bool): Self-position considering.
CDC (float): Decides how many dimensions will be varied.
SRD (float): Seeking range of the selected dimension.
vMax (float): Maximal velocity.
See Also:
* :func:`NiaPy.algorithms.Algorithm.setParameters`
"""
Algorithm.setParameters(self, NP=NP, **ukwargs)
self.MR, self.C1, self.SMP, self.SPC, self.CDC, self.SRD, self.vMax = MR, C1, SMP, SPC, CDC, SRD, vMax
def setParameters(self, NP=25, Ts=5, Mr=0.25, Cr=0.25, Selection=TournamentSelection, Crossover=UniformCrossover, Mutation=UniformMutation, **ukwargs):
r"""Set the parameters of the algorithm.
Arguments:
NP (Optional[int]): Population size.
Ts (Optional[int]): Tournament selection.
Mr (Optional[int]): Mutation rate.
Cr (Optional[float]): Crossover rate.
Selection (Optional[Callable[[numpy.ndarray[Individual], int, int, Individual, mtrand.RandomState], Individual]]): Selection operator.
Crossover (Optional[Callable[[numpy.ndarray[Individual], int, float, mtrand.RandomState], Individual]]): Crossover operator.
Mutation (Optional[Callable[[numpy.ndarray[Individual], int, float, Task, mtrand.RandomState], Individual]]): Mutation operator.
See Also:
* :func:`NiaPy.algorithms.Algorithm.setParameters`
* Selection:
* :func:`NiaPy.algorithms.basic.TournamentSelection`
* :func:`NiaPy.algorithms.basic.RouletteSelection`
* Crossover:
* :func:`NiaPy.algorithms.basic.UniformCrossover`
* :func:`NiaPy.algorithms.basic.TwoPointCrossover`
* :func:`NiaPy.algorithms.basic.MultiPointCrossover`
* :func:`NiaPy.algorithms.basic.CrossoverUros`
* Mutations:
* :func:`NiaPy.algorithms.basic.UniformMutation`
* :func:`NiaPy.algorithms.basic.CreepMutation`
* :func:`NiaPy.algorithms.basic.MutationUros`
"""
Algorithm.setParameters(self, NP=NP, itype=ukwargs.pop('itype', Individual), InitPopFunc=ukwargs.pop('InitPopFunc', defaultIndividualInit), **ukwargs)
self.Ts, self.Mr, self.Cr = Ts, Mr, Cr
self.Selection, self.Crossover, self.Mutation = Selection, Crossover, Mutation
class AlgorithBaseTestCase(TestCase):
def setUp(self):
self.a = Algorithm()
def test_randint(self):
o = self.a.randint(Nmax=20, Nmin=10, D=[10, 10])
self.assertEqual(o.shape, (10, 10))
o = self.a.randint(Nmax=20, Nmin=10, D=(10, 5))
self.assertEqual(o.shape, (10, 5))
o = self.a.randint(Nmax=20, Nmin=10, D=10)
self.assertEqual(o.shape, (10, ))
def test_setParameters(self):
self.a.setParameters(t=None, a=20)
self.assertRaises(AttributeError,
lambda: self.assertEqual(self.a.a, None))
def test_randn(self):
a = self.a.randn([1, 2])
self.assertEqual(a.shape, (1, 2))
a = self.a.randn(1)
self.assertEqual(len(a), 1)
a = self.a.randn(2)
self.assertEqual(len(a), 2)
a = self.a.randn()
self.assertIsInstance(a, float)
def setParameters(self, NP=50, N_max=0.01, V_f=0.02, D_max=0.002, C_t=0.93, W_n=0.42, W_f=0.38, d_s=2.63, nn=5, Cr=0.2, Mu=0.05, epsilon=1e-31, **ukwargs):
r"""Set the arguments of an algorithm.
Arguments:
NP (Optional[int]): Number of krill herds in population.
N_max (Optional[float]): Maximum induced speed.
V_f (Optional[float]): Foraging speed.
D_max (Optional[float]): Maximum diffusion speed.
C_t (Optional[float]): Constant $\in [0, 2]$.
W_n (Optional[Union[int, float, numpy.ndarray]]): Intera weights of the motion induced from neighbors :math:`\in [0, 1]`.
W_f (Optional[Union[int, float, numpy.ndarray]]): Intera weights of the motion induced from foraging :math:`\in [0, 1]`.
d_s (Optional[float]): Maximum euclidean distance for neighbors.
nn (Optional[int]): Maximum neighbors for neighbors effect.
Cr (Optional[float]): Crossover probability.
Mu (Optional[float]): Mutation probability.
epsilon (Optional[float]): Small numbers for division.
See Also:
* :func:`NiaPy.algorithms.algorithm.Algorithm.setParameters`
"""
Algorithm.setParameters(self, **ukwargs)
self.NP = NP
self.N_max, self.V_f, self.D_max, self.C_t, self.W_n, self.W_f, self.d_s, self.nn, self._Cr, self._Mu, self.epsilon = N_max, V_f, D_max, C_t, W_n, W_f, d_s, nn, Cr, Mu, epsilon
if ukwargs: logger.info('Unused arguments: %s' % (ukwargs))
def setParameters(self,
NP=50,
F=1,
CR=0.8,
CrossMutt=CrossRand1,
**ukwargs):
r"""Set the algorithm parameters.
Arguments:
NP (Optional[int]): Population size.
F (Optional[float]): Scaling factor.
CR (Optional[float]): Crossover rate.
CrossMutt (Optional[Callable[[numpy.ndarray, int, numpy.ndarray, float, float, mtrand.RandomState, list], numpy.ndarray]]): Crossover and mutation strategy.
ukwargs (Dict[str, Any]): Additional arguments.
See Also:
* :func:`NiaPy.algorithms.Algorithm.setParameters`
"""
Algorithm.setParameters(self,
NP=NP,
InitPopFunc=ukwargs.pop(
'InitPopFunc', defaultIndividualInit),
itype=ukwargs.pop('itype', Individual),
**ukwargs)
self.F, self.CR, self.CrossMutt = F, CR, CrossMutt
if ukwargs: logger.info('Unused arguments: %s' % (ukwargs))
def setParameters(self,
NP=43,
alpha=(1, 0.83),
gamma=(1.17, 0.56),
theta=(0.932, 0.832),
d=euclidean,
dn=euclidean,
nl=1,
F=1.2,
CR=0.25,
Combination=Elitism,
**ukwargs):
r"""Set the parameters for the algorith.
Arguments:
alpha (Optional[List[float]]): Factor for fickleness index function :math:`\in [0, 1]`.
gamma (Optional[List[float]]): Factor for external irregularity index function :math:`\in [0, \infty)`.
theta (Optional[List[float]]): Factor for internal irregularity index function :math:`\in [0, \infty)`.
d (Optional[Callable[[float, float], float]]): function that takes two arguments that are function values and calcs the distance between them.
dn (Optional[Callable[[numpy.ndarray, numpy.ndarray], float]]): function that takes two arguments that are points in function landscape and calcs the distance between them.
nl (Optional[float]): Normalized range for neighborhood search :math:`\in (0, 1]`.
F (Optional[float]): Mutation parameter.
CR (Optional[float]): Crossover parameter :math:`\in [0, 1]`.
Combination (Optional[Callable[numpy.ndarray, numpy.ndarray, numpy.ndarray, numpy.ndarray, float, float, float, float, float, float, Task, mtrand.RandomState]]): Function for combining individuals to get new position/individual.
See Also:
* :func:`NiaPy.algorithms.Algorithm.setParameters`
* Combination methods:
* :func:`NiaPy.algorithms.other.Elitism'
* :func:`NiaPy.algorithms.other.Crossover`
* :func:`NiaPy.algorithms.other.Sequential`
"""
Algorithm.setParameters(self, NP=NP, **ukwargs)
self.alpha, self.gamma, self.theta, self.d, self.dn, self.nl, self.F, self.CR, self.Combination = alpha, gamma, theta, d, dn, nl, F, CR, Combination
def setParameters(self,
NP=25,
C1=2.0,
C2=2.0,
w=0.7,
vMin=-1.5,
vMax=1.5,
Repair=repair.reflect,
**ukwargs):
r"""Set Particle Swarm Algorithm main parameters.
Args:
NP (int): Population size
C1 (float): Cognitive component.
C2 (float): Social component.
w (Union[float, numpy.ndarray]): Inertial weight.
vMin (Union[float, numpy.ndarray]): Minimal velocity.
vMax (Union[float, numpy.ndarray]): Maximal velocity.
Repair (Callable[[np.ndarray, np.ndarray, np.ndarray, dict], np.ndarray]): Repair method for velocity.
**ukwargs: Additional arguments
See Also:
* :func:`NiaPy.algorithms.Algorithm.setParameters`
"""
Algorithm.setParameters(self, NP=NP, **ukwargs)
self.C1, self.C2, self.w, self.vMin, self.vMax, self.Repair = C1, C2, w, vMin, vMax, Repair
def setParameters(self,
NP=50,
omega=0.25,
mu=0.5,
alpha=0.5,
S_init=10,
E_init=10,
T_min=-10,
T_max=10,
**ukwargs):
r"""Set the arguments of an algorithm.
Arguments:
NP (Optional[int]): Population size :math:`\in [1, \infty)`.
T_min (Optional[float]): Minimum temperature, must be true :math:`$T_{min} < T_{max}`.
T_max (Optional[float]): Maximum temperature, must be true :math:`T_{min} < T_{max}`.
omega (Optional[float]): Burden factor :math:`\in [0, 1]`.
mu (Optional[float]): Dying rate :math:`\in [0, 1]`.
S_init (Optional[float]): Initial supply :math:`\in (0, \infty)`.
E_init (Optional[float]): Initial endurance :math:`\in (0, \infty)`.
See Also:
* :func:`NiaPy.algorithms.Algorithm.setParameters`
"""
Algorithm.setParameters(self,
NP=NP,
itype=Camel,
InitPopFunc=ukwargs.pop(
'InitPopFunc', self.initPop),
**ukwargs)
self.omega, self.mu, self.alpha, self.S_init, self.E_init, self.T_min, self.T_max = omega, mu, alpha, S_init, E_init, T_min, T_max
def setParameters(self,
NP=None,
alpha=0.1,
gamma=0.3,
rho=-0.2,
sigma=-0.2,
**ukwargs):
r"""Set the arguments of an algorithm.
Arguments:
NP (Optional[int]): Number of individuals.
alpha (Optional[float]): Reflection coefficient parameter
gamma (Optional[float]): Expansion coefficient parameter
rho (Optional[float]): Contraction coefficient parameter
sigma (Optional[float]): Shrink coefficient parameter
See Also:
* :func:`NiaPy.algorithms.Algorithm.setParameters`
"""
Algorithm.setParameters(self,
NP=NP,
InitPopFunc=ukwargs.pop(
'InitPopFunc', self.initPop),
**ukwargs)
self.alpha, self.gamma, self.rho, self.sigma = alpha, gamma, rho, sigma
def setParameters(self,
NP=10,
lt=3,
al=10,
lsc=1,
gsc=1,
tr=0.3,
**ukwargs):
r"""Set the parameters of the algorithm.
Args:
NP (Optional[int]): Population size.
lt (Optional[int]): Life time parameter.
al (Optional[int]): Area limit parameter.
lsc (Optional[int]): Local seeding changes parameter.
gsc (Optional[int]): Global seeding changes parameter.
tr (Optional[float]): Transfer rate parameter.
ukwargs (Dict[str, Any]): Additional arguments.
See Also:
* :func:`NiaPy.algorithms.Algorithm.setParameters`
"""
Algorithm.setParameters(self, NP=NP)
self.lt, self.al, self.lsc, self.gsc, self.tr = lt, al, lsc, gsc, tr
if ukwargs: logger.info('Unused arguments: %s' % (ukwargs))
def setParameters(self,
NP=25,
C1=2.0,
C2=2.0,
w=0.7,
vMin=-4,
vMax=4,
**ukwargs):
r"""Set Particle Swarm Algorithm main parameters.
Args:
NP (Optional[int]): Population size
C1 (Optional[float]): Cognitive component
C2 (Optional[float]): Social component
w (Optional[float]): Inertial weight
vMin (Optional[float]): Mininal velocity
vMax (Optional[float]): Maximal velocity
**ukwargs: Additional arguments
See Also:
* :func:`NiaPy.algorithms.Algorithm.setParameters`
"""
Algorithm.setParameters(self, NP=NP, **ukwargs)
self.C1, self.C2, self.w, self.vMin, self.vMax = C1, C2, w, vMin, vMax
if ukwargs: logger.info('Unused arguments: %s' % (ukwargs))
def setParameters(self,
M=40,
NoLsTests=5,
NoLs=5,
NoLsBest=5,
NoEnabled=17,
BONUS1=10,
BONUS2=1,
LSs=(MTS_LS1v1, MTS_LS2, MTS_LS3v1),
**ukwargs):
r"""Set the arguments of the algorithm.
Arguments:
M (int): Number of individuals in population.
NoLsTests (int): Number of test runs on local search algorithms.
NoLs (int): Number of local search algorithm runs.
NoLsBest (int): Number of locals search algorithm runs on best solution.
NoEnabled (int): Number of best solution for testing.
BONUS1 (int): Bonus for improving global best solution.
BONUS2 (int): Bonus for improving self.
LSs (Iterable[Callable[[numpy.ndarray, float, numpy.ndarray, float, bool, numpy.ndarray, Task, Dict[str, Any]], Tuple[numpy.ndarray, float, numpy.ndarray, float, bool, int, numpy.ndarray]]]): Local searches to use.
See Also:
* :func:`NiaPy.algorithms.Algorithm.setParameters`
"""
Algorithm.setParameters(self, NP=M)
self.NoLsTests, self.NoLs, self.NoLsBest, self.NoEnabled, self.BONUS1, self.BONUS2 = NoLsTests, NoLs, NoLsBest, NoEnabled, BONUS1, BONUS2
self.LSs = LSs
if ukwargs: logger.info('Unused arguments: %s' % (ukwargs))
def setParameters(self, epsilon=1e-20, **ukwargs):
r"""Set core parameters of CovarianceMatrixAdaptionEvolutionStrategy algorithm.
Args:
epsilon (float): Small number.
**ukwargs (Dict[str, Any]): Additional arguments.
"""
Algorithm.setParameters(self, **ukwargs)
self.epsilon = epsilon
def setParameters(self, delta=0.5, Neighborhood=Neighborhood, **ukwargs):
r"""Set the algorithm parameters/arguments.
Args:
* delta (Optional[float]): Change for searching in neighborhood.
* Neighborhood (Optional[Callable[numpy.ndarray, float, Task], Tuple[numpy.ndarray, float]]]): Function for getting neighbours.
"""
Algorithm.setParameters(self, NP=1, **ukwargs)
self.delta, self.Neighborhood = delta, Neighborhood
def setParameters(self):
r"""Set the algorithm parameters/arguments.
Arguments:
See Also
* :func:`NiaPy.algorithms.Algorithm.setParameters`
"""
Algorithm.setParameters(self, NP=1)
def setParameters(self, NP=25, **ukwargs):
r"""Set the algorithm parameters.
Arguments:
NP (int): Number of individuals in population
See Also:
* :func:`NiaPy.algorithms.algorithm.Algorithm.setParameters`
"""
Algorithm.setParameters(self, NP=NP, **ukwargs)
def setParameters(self, **ukwargs):
r"""Set the algorithm parameters/arguments.
Arguments:
See Also
* :func:`NiaPy.algorithms.Algorithm.setParameters`
"""
ukwargs.pop('NP', None)
Algorithm.setParameters(self, NP=1)
def setParameters(self, n=10, C_a=1.5, C_r=0.5, **ukwargs):
r"""Set the arguments of an algorithm.
Arguments:
n (int): Number of sparks :math:`\in [1, \infty)`.
C_a (float): Amplification coefficient :math:`\in [1, \infty)`.
C_r (float): Reduction coefficient :math:`\in (0, 1)`.
"""
Algorithm.setParameters(self, **ukwargs)
self.n, self.C_a, self.C_r = n, C_a, C_r
if ukwargs: logger.info('Unused arguments: %s' % (ukwargs))
def setParameters(self, **ukwargs): r"""Set the parameters of the algorithm. Args: A (Optional[float]): Loudness. r (Optional[float]): Pulse rate. See Also: * :func:`NiaPy.algorithms.Algorithm.setParameters` """ Algorithm.setParameters(self, NP=80, **ukwargs) self.A, self.r = 0.9, 0.1
def setParameters(self, NP=40, levy=0.01, **ukwargs):
r"""Set the parameters of the algorithm.
Args:
levy (Optional[float]): Levy factor.
See Also:
* :func:`NiaPy.algorithms.Algorithm.setParameters`
"""
Algorithm.setParameters(self, NP=NP, **ukwargs)
self.levy = levy
def setParameters(self, delta=0.5, Neighborhood=Neighborhood, **ukwargs):
r"""Set the algorithm parameters/arguments.
Args:
* delta (float): TODO
* Neighborhood (Callable[numpy.ndarray, float, Task], Tuple[numpy.ndarray, float]]): Function for getting neighbours.
"""
Algorithm.setParameters(self, **ukwargs)
self.delta, self.Neighborhood = delta, Neighborhood
if ukwargs: logger.info('Unused arguments: %s' % (ukwargs))
def setParameters(self, n=10, C_a=1.5, C_r=0.5, **ukwargs):
r"""Set the arguments of an algorithm.
Arguments:
n (int): Number of sparks :math:`\in [1, \infty)`.
C_a (float): Amplification coefficient :math:`\in [1, \infty)`.
C_r (float): Reduction coefficient :math:`\in (0, 1)`.
"""
ukwargs.pop('NP', None)
Algorithm.setParameters(self, NP=1, **ukwargs)
self.n, self.C_a, self.C_r = n, C_a, C_r
def setParameters(self, NP=40, G_0=2.467, epsilon=1e-17, **ukwargs):
r"""Set the algorithm parameters.
Arguments:
G_0 (float): Starting gravitational constant.
epsilon (float): TODO.
See Also:
* :func:`NiaPy.algorithms.algorithm.Algorithm.setParameters`
"""
Algorithm.setParameters(self, NP=NP, **ukwargs)
self.G_0, self.epsilon = G_0, epsilon
def setParameters(self, NP=10, Limit=100, **ukwargs): r"""Set the parameters of Artificial Bee Colony Algorithm. Parameters: Limit (Optional[Union[float, numpy.ndarray[float]]]): Limt **ukwargs (Dict[str, Any]): Additional arguments See Also: * :func:`NiaPy.algorithms.Algorithm.setParameters` """ Algorithm.setParameters(self, NP=NP, InitPopFunc=defaultIndividualInit, itype=SolutionABC, **ukwargs) self.FoodNumber, self.Limit = int(self.NP / 2), Limit
def setParameters(self, NP=10, Limit=100, **ukwargs):
r"""Set the parameters of Artificial Bee Colony Algorithm.
Parameters:
Limit (Optional[Union[float, numpy.ndarray[float]]]): Limt
**ukwargs (Dict[str, Any]): Additional arguments
See Also:
* :func:`NiaPy.algorithms.Algorithm.setParameters`
"""
Algorithm.setParameters(self, NP=NP, itype=SolutionABC, **ukwargs)
self.FoodNumber, self.Limit = int(self.NP / 2), Limit
if ukwargs: logger.info('Unused arguments: %s' % (ukwargs))
def setParameters(self, NP=40, A=0.5, r=0.5, Qmin=0.0, Qmax=2.0, **ukwargs): r"""Set the parameters of the algorithm. Args: A (Optional[float]): Loudness. r (Optional[float]): Pulse rate. Qmin (Optional[float]): Minimum frequency. Qmax (Optional[float]): Maximum frequency. See Also: * :func:`NiaPy.algorithms.Algorithm.setParameters` """ Algorithm.setParameters(self, NP=NP, **ukwargs) self.A, self.r, self.Qmin, self.Qmax = A, r, Qmin, Qmax
