def set_parameters(self,
n=25,
c1=2.0,
c2=2.0,
w=0.7,
min_velocity=-1.5,
max_velocity=1.5,
repair=reflect_repair,
**ukwargs):
r"""Set Particle Swarm Algorithm main parameters.
Args:
n (int): Population size
c1 (float): Cognitive component.
c2 (float): Social component.
w (Union[float, numpy.ndarray]): Inertial weight.
min_velocity (Union[float, numpy.ndarray]): Minimal velocity.
max_velocity (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.set_parameters(self, n=n, **ukwargs)
self.C1, self.C2, self.w, self.vMin, self.vMax, self.Repair = c1, c2, w, min_velocity, max_velocity, repair
def set_parameters(self,
n=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:
n (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:`WeOptPy.algorithms.Algorithm.setParameters`
"""
Algorithm.set_parameters(self, n=n, **ukwargs)
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 set_parameters(self,
n=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:
n (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.set_parameters(self, n=n, **ukwargs)
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
def set_parameters(self,
n=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:
n (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:`WeOptPy.algorithms.Algorithm.setParameters`
"""
Algorithm.set_parameters(self, n=n, **ukwargs)
self.MR, self.C1, self.SMP, self.SPC, self.CDC, self.SRD, self.vMax = MR, C1, SMP, SPC, CDC, SRD, vMax
def set_parameters(self, n=25, **ukwargs):
r"""Set the algorithm parameters.
Arguments:
n (int): Number of individuals in population
See Also:
* :func:`NiaPy.algorithms.algorithm.Algorithm.setParameters`
"""
Algorithm.set_parameters(self, n=n, **ukwargs)
def set_parameters(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('n', None)
Algorithm.set_parameters(self, n=1, **ukwargs)
self.n, self.C_a, self.C_r = n, C_a, C_r
def set_parameters(self, n=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.set_parameters(self, n=n, **ukwargs)
self.G_0, self.epsilon = G_0, epsilon
def set_parameters(self, n=20, PAR=5.0 / 12.0, PER=1.2, **ukwargs): r"""Set the parameters of the algorithm. Args: n (Optional[int]): Population size. PAR (Optional[int]): Partition. PER (Optional[int]): Period. ukwargs (Dict[str, Any]): Additional arguments. See Also: * :func:`NiaPy.algorithms.Algorithm.setParameters` """ Algorithm.set_parameters(self, n=n, **ukwargs) self.NP, self.PAR, self.PER, self.keep, self.BAR, self.NP1 = n, PAR, PER, 2, PAR, int(np.ceil(PAR * n)) self.NP2 = int(n - self.NP1)
def set_parameters(self, n=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:
n (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:`WeOptPy.algorithms.Algorithm.setParameters`
"""
Algorithm.set_parameters(self, n=n, itype=Camel, init_pop_func=ukwargs.pop('init_pop_func', self.init_pop), **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 set_parameters(self, n=10, lt=3, al=10, lsc=1, gsc=1, tr=0.3, **ukwargs): r"""Set the parameters of the algorithm. Args: n (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:`WeOptPy.algorithms.Algorithm.setParameters` """ Algorithm.set_parameters(self, n=n, **ukwargs) self.lt, self.al, self.lsc, self.gsc, self.tr = lt, al, lsc, gsc, tr
def set_parameters(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.
"""
ukwargs.pop('n', None)
Algorithm.set_parameters(self, n=n, **ukwargs)
self.l0, self.nt, self.rho, self.gamma, self.beta, self.s, self.Distance = l0, nt, rho, gamma, beta, s, Distance
def set_parameters(self,
n=25,
ts=5,
mr=0.25,
cr=0.25,
selection=tournament_selection,
crossover=uniform_crossover,
mutation=uniform_mutation,
**ukwargs):
r"""Set the parameters of the algorithm.
Arguments:
n (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:`WeOptPy.algorithms.Algorithm.setParameters`
* Selection:
* :func:`WeOptPy.algorithms.TournamentSelection`
* :func:`WeOptPy.algorithms.RouletteSelection`
* Crossover:
* :func:`WeOptPy.algorithms.UniformCrossover`
* :func:`WeOptPy.algorithms.TwoPointCrossover`
* :func:`WeOptPy.algorithms.MultiPointCrossover`
* :func:`WeOptPy.algorithms.CrossoverUros`
* Mutations:
* :func:`WeOptPy.algorithms.UniformMutation`
* :func:`WeOptPy.algorithms.CreepMutation`
* :func:`WeOptPy.algorithms.MutationUros`
"""
Algorithm.set_parameters(self,
n=n,
itype=ukwargs.pop('itype', Individual),
init_pop_func=ukwargs.pop(
'init_pop_func', default_individual_init),
**ukwargs)
self.Ts, self.Mr, self.Cr = ts, mr, cr
self.Selection, self.Crossover, self.Mutation = selection, crossover, mutation
def set_parameters(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:`WeOptPy.algorithms.Algorithm.setParameters`
"""
ukwargs.pop('n', None)
Algorithm.set_parameters(self, n=HMS, **ukwargs)
self.r_accept, self.r_pa, self.b_range = r_accept, r_pa, b_range
def set_parameters(self,
N=40,
m=40,
a=1,
b=2,
A=40,
epsilon=1e-31,
**ukwargs):
r"""Set the arguments of an algorithm.
Arguments:
N (int): Number of Fireworks
m (int): Number of sparks
a (int): Limitation of sparks
b (int): Limitation of sparks
A (float): TODO.
epsilon (float): Small number for non 0 devision
"""
ukwargs.pop('n', None)
Algorithm.set_parameters(self, n=N, **ukwargs)
self.m, self.a, self.b, self.A, self.epsilon = m, a, b, A, epsilon
def set_parameters(self, n=40, f=0.7, r=0.3, c=3, fc=0.5, **ukwargs):
r"""Set Monkey King Evolution v1 algorithms static parameters.
Args:
n (int): Population size.
f (float): Scale factor for normal particle.
r (float): Percentage value of now many new particle Monkey King particle creates. Value in rage [0, 1].
c (int): Number of new particles generated by Monkey King particle.
fc (float): Scale factor for Monkey King particles.
ukwargs (Dict[str, Any]): Additional arguments.
See Also:
* :func:`NiaPy.algorithms.algorithm.Algorithm.setParameters`
"""
Algorithm.set_parameters(self,
n=n,
itype=ukwargs.pop('itype', MkeSolution),
init_pop_func=ukwargs.pop(
'init_pop_func', default_individual_init),
**ukwargs)
self.F, self.R, self.C, self.FC = f, r, c, fc
def set_parameters(self,
N=25,
phi=0.4,
Fa=0.5,
Fb=0.5,
Fd=0.3,
k=25,
P_Cr=0.5,
P_F=0.36,
SexualCrossover=sexual_crossover_simple,
Brooding=brooding_simple,
Distance=euclidean,
**ukwargs):
r"""Set the parameters of the algorithm.
Arguments:
N (int): population size for population initialization.
phi (int): TODO.
Fa (float): Value $\in [0, 1]$ for Asexual reproduction size.
Fb (float): Value $\in [0, 1]$ for Brooding size.
Fd (float): Value $\in [0, 1]$ for Depredation size.
k (int): Trys for larvae setting.
SexualCrossover (Callable[[numpy.ndarray, float, Task, mtrand.RandomState, Dict[str, Any]], Tuple[numpy.ndarray, numpy.ndarray]]): Crossover function.
P_Cr (float): Crossover rate $\in [0, 1]$.
Brooding (Callable[[numpy.ndarray, float, Task, mtrand.RandomState, dict], Tuple[numpy.ndarray, numpy.ndarray]]): 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:`WeOptPy.algorithms.Algorithm.setParameters`
"""
ukwargs.pop('n', None)
Algorithm.set_parameters(self, n=N, **ukwargs)
self.phi, self.k, self.P_Cr, self.P_F = phi, k, P_Cr, P_F
self.Fa, self.Fb, self.Fd = int(self.NP * Fa), int(self.NP * Fb), int(
self.NP * Fd)
self.SexualCrossover, self.Brooding, self.Distance = SexualCrossover, Brooding, Distance