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=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=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 init_population(self, task):
r"""Initialize starting population.
Args:
task (Task): Optimization task
Returns:
Tuple[numpy.ndarray, numpy.ndarray, list, dict]:
1. Initialized population
2. Initialized population function/fitness values
3. Additional arguments:
4. Additional keyword arguments:
* best_flames (numpy.ndarray): Best individuals
* best_flame_fitness (numpy.ndarray): Best individuals fitness/function values
* previous_population (numpy.ndarray): Previous population
* previous_fitness (numpy.ndarray[float]): Previous population fitness/function values
See Also:
* :func:`NiaPy.algorithms.algorithm.Algorithm.initPopulation`
"""
moth_pos, moth_fitness, args, d = Algorithm.init_population(self, task)
# Create best population
indexes = np.argsort(moth_fitness)
best_flames, best_flame_fitness = moth_pos[indexes], moth_fitness[
indexes]
# Init previous population
previous_population, previous_fitness = np.zeros(
(self.NP, task.D)), np.zeros(self.NP)
d.update({
'best_flames': best_flames,
'best_flame_fitness': best_flame_fitness,
'previous_population': previous_population,
'previous_fitness': previous_fitness
})
return moth_pos, moth_fitness, args, d
def init_population(self, task):
r"""Initialize population and dynamic arguments of the Particle Swarm Optimization algorithm.
Args:
task: Optimization task.
Returns:
Tuple[numpy.ndarray, numpy.ndarray, list, dict]:
1. Initial population.
2. Initial population fitness/function values.
3. Additional arguments.
4. Additional keyword arguments:
* popb (numpy.ndarray): particles best population.
* fpopb (numpy.ndarray[float]): particles best positions function/fitness value.
* w (numpy.ndarray): Inertial weight.
* min_velocity (numpy.ndarray): Minimal velocity.
* max_velocity (numpy.ndarray): Maximal velocity.
* V (numpy.ndarray): Initial velocity of particle.
See Also:
* :func:`NiaPy.algorithms.Algorithm.initPopulation`
"""
pop, fpop, args, d = Algorithm.init_population(self, task)
d.update(self.init(task))
d.update({'popb': pop.copy(), 'fpopb': fpop.copy()})
return pop, fpop, args, d
def init_population(self, task):
r"""Initialize the first herd/population.
Args:
task (Task): Optimization task.
Returns:
Tuple[numpy.ndarray, numpy.ndarray, list, dict]:
1. Initialized herd/population.
2. Initialized herd/populations function/fitness values.
3. Additional arguments.
4. Additional keyword arguments:
* KHo (numpy.ndarray): TODO
* KHo_f (float): TODO
* N (numpy.ndarray): TODO
* F (numpy.ndarray): TODO
* Dt (numpy.ndarray): TODO
See Also:
* :func:`NiaPy.algorithms.Algorithm.initPopulation`
"""
KH, KH_f, args, kwargs = Algorithm.init_population(self, task)
KHo, KHo_f = np.full([self.NP, task.D],
task.optType.value * np.inf), np.full(
self.NP, task.optType.value * np.inf)
N, F, Dt = np.full(self.NP, .0), np.full(self.NP,
.0), np.mean(task.range()) / 2
kwargs.update({'KHo': KHo, 'KHo_f': KHo_f, 'N': N, 'F': F, 'Dt': Dt})
return KH, KH_f, args, kwargs
def init_population(self, task):
r"""Initialize stating population.
Args:
task (Task): Optimization task.
Returns:
Tuple[numpy.ndarray, numpy.ndarray, list, dict]:
1. Initialized population.
2. Initialized populations function/fitness values.
3. Additional arguments.
4. Additional keyword arguments:
* W_n (numpy.ndarray): Weights neighborhood.
* W_f (numpy.ndarray): Weights foraging.
* N (numpy.ndarray): TODO
* F (numpy.ndarray): TODO
See Also:
* :func:`NiaPy.algorithms.algorithm.Algorithm.initPopulation`
"""
KH, KH_f, args, kwargs = Algorithm.init_population(self, task)
W_n, W_f = self.init_weights(task)
N, F = np.full(self.NP, .0), np.full(self.NP, .0)
kwargs.update({'W_n': W_n, 'W_f': W_f, 'N': N, 'F': F})
return KH, KH_f, args, kwargs
def type_parameters():
r"""Get dictionary with functions for checking values of parameters.
Returns:
Dict[str, Callable]:
* omega (Callable[[Union[int, float]], bool])
* mu (Callable[[float], bool])
* alpha (Callable[[float], bool])
* S_init (Callable[[Union[float, int]], bool])
* E_init (Callable[[Union[float, int]], bool])
* T_min (Callable[[Union[float, int], bool])
* T_max (Callable[[Union[float, int], bool])
See Also:
* :func:`WeOptPy.algorithms.Algorithm.typeParameters`
"""
d = Algorithm.type_parameters()
d.update({
'omega': lambda x: isinstance(x, (float, int)),
'mu': lambda x: isinstance(x, float) and 0 <= x <= 1,
'alpha': lambda x: isinstance(x, float) and 0 <= x <= 1,
'S_init': lambda x: isinstance(x, (float, int)) and x > 0,
'E_init': lambda x: isinstance(x, (float, int)) and x > 0,
'T_min': lambda x: isinstance(x, (float, int)) and x > 0,
'T_max': lambda x: isinstance(x, (float, int)) and x > 0
})
return d
def type_parameters():
r"""Get dictionary with functions for checking values of parameters.
Returns:
Dict[str, Callable]:
* N_max (Callable[[Union[int, float]], bool])
* V_f (Callable[[Union[int, float]], bool])
* D_max (Callable[[Union[int, float]], bool])
* C_t (Callable[[Union[int, float]], bool])
* W_n (Callable[[Union[int, float]], bool])
* W_f (Callable[[Union[int, float]], bool])
* d_s (Callable[[Union[int, float]], boool])
* nn (Callable[[int], bool])
* Cr (Callable[[float], bool])
* Mu (Callable[[float], bool])
* epsilon (Callable[[float], bool])
See Also:
* :func:`NiaPy.algorithms.algorithm.Algorithm`
"""
d = Algorithm.type_parameters()
d.update({
'N_max': lambda x: isinstance(x, (int, float)) and x > 0,
'V_f': lambda x: isinstance(x, (int, float)) and x > 0,
'D_max': lambda x: isinstance(x, (int, float)) and x > 0,
'C_t': lambda x: isinstance(x, (int, float)) and x > 0,
'W_n': lambda x: isinstance(x, (int, float)) and x > 0,
'W_f': lambda x: isinstance(x, (int, float)) and x > 0,
'd_s': lambda x: isinstance(x, (int, float)) and x > 0,
'nn': lambda x: isinstance(x, int) and x > 0,
'Cr': lambda x: isinstance(x, float) and 0 <= x <= 1,
'Mu': lambda x: isinstance(x, float) and 0 <= x <= 1,
'epsilon': lambda x: isinstance(x, float) and 0 < x < 1
})
return d
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=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 type_parameters():
r"""Get dictionary with functions for checking values of parameters.
Returns:
Dict[str, Callable]: TODO
See Also:
* :func:`NiaPy.algorithms.algorithm.Algorithm.typeParameters`
"""
return Algorithm.type_parameters()
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, 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=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 get_parameters(self):
r"""Get algorithms parameters values.
Returns:
Dict[str, Any]: Dictionary of parameters name and value.
See Also:
* :func:`NiaPy.algorithms.Algorithm.getParameters`
"""
d = Algorithm.get_parameters(self)
d.update({'F': self.F, 'R': self.R, 'C': self.C, 'FC': self.FC})
return d
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
def get_parameters(self):
r"""Get parameters values of the algorithm.
Returns:
Dict[str, Any]: TODO.
"""
d = Algorithm.get_parameters(self)
d.update({
'lt': self.lt,
'al': self.al,
'lsc': self.lsc,
'gsc': self.gsc,
'tr': self.tr
})
return d
def get_parameters(self):
r"""Get algorithm parameters values.
Returns:
Dict[str, Any]: TODO.
See Also:
* :func:`NiaPy.algorithms.algorithm.Algorithm.getParameters`
"""
d = Algorithm.get_parameters(self)
d.update({
'G_0': self.G_0,
'epsilon': self.epsilon
})
return d
def type_parameters():
r"""Get dictionary with functions for checking values of parameters.
Returns:
Dict[str, Callable]:
* PAR (Callable[[float], bool]): Checks if partition parameter has a proper value.
* PER (Callable[[float], bool]): Checks if period parameter has a proper value.
See Also:
* :func:`NiaPy.algorithms.algorithm.Algorithm.typeParameters`
"""
d = Algorithm.type_parameters()
d.update({
'PAR': lambda x: isinstance(x, float) and x > 0,
'PER': lambda x: isinstance(x, float) and x > 0
})
return d
def get_parameters(self):
r"""Get parameters values for the algorithm.
Returns:
Dict[str, Any]: TODO.
"""
d = Algorithm.get_parameters(self)
d.update({
'PAR': self.PAR,
'PER': self.PER,
'keep': self.keep,
'BAR': self.BAR,
'NP1': self.NP1,
'NP2': self.NP2
})
return d
def get_parameters(self):
r"""Get parameters of the algorithm.
Returns:
Dict[str, Any]:
"""
d = Algorithm.get_parameters(self)
d.update({
'omega': self.omega,
'mu': self.mu,
'alpha': self.alpha,
'S_init': self.S_init,
'E_init': self.E_init,
'T_min': self.T_min,
'T_max': self.T_max
})
return d
def type_parameters():
r"""TODO.
Returns:
Dict[str, Callable]:
* G_0 (Callable[[Union[int, float]], bool]): TODO
* epsilon (Callable[[float], bool]): TODO
See Also:
* :func:`NiaPy.algorithms.algorithm.Algorithm.typeParameters`
"""
d = Algorithm.type_parameters()
d.update({
'G_0': lambda x: isinstance(x, (int, float)) and x >= 0,
'epsilon': lambda x: isinstance(x, float) and 0 < x < 1
})
return d