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 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 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 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 init_population(self, task):
r"""Initialize starting population.
Args:
task (Task): Optimization task.
Returns:
Tuple[numpy.ndarray, float, list, dict]:
1. Initial solution.
2. Initial solution function/fitness value.
3. Additional arguments.
4. Additional keyword arguments:
* a (numpy.ndarray): Starting amplitude or search range.
"""
x, x_fit, args, d = Algorithm.init_population(self, task)
d.update({'a': task.bRange})
return x, x_fit, args, d
def init_population(self, task):
r"""Initialize first population.
Args:
task (Task): Optimization task.
Returns:
Tuple[numpy.ndarray, numpy.ndarray, list, dict]:
1. New harmony/population.
2. New population fitness/function values.
3. Additional parameters.
4. Additional keyword parameters.
See Also:
* :func:`WeOptPy.algorithms.Algorithm.initPopulation`
"""
return Algorithm.init_population(self, task)
def init_population(self, task): r"""Initialize population. Args: task (Task): Optimization task. Returns: Tuple[numpy.ndarray, numpy.ndarray, list, dict]: 1. New population of Camels. 2. New population fitness/function values. 3. Additional arguments. 4. Additional keyword arguments. See Also: * :func:`WeOptPy.algorithms.Algorithm.initPopulation` """ caravan, fcaravan, args, kwargs = Algorithm.init_population(self, task) return caravan, fcaravan, args, kwargs
def init_population(self, task):
r"""Initialize 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:
* Ah (): TODO
* Ab (): TODO
"""
FW, FW_f, args, _ = Algorithm.init_population(self, task)
Ah, Ab = self.init_amplitude(task)
return FW, FW_f, args, {'Ah': Ah, 'Ab': Ab}
def init_population(self, task):
r"""Initialize staring population.
Args:
task (Task): Optimization task.
Returns:
Tuple[numpy.ndarray, numpy.ndarray, Dict[str, Any]]:
1. Initialized population.
2. Initialized populations fitness/function values.
3. Additional arguments:
* v (numpy.ndarray): TODO
See Also:
* :func:`NiaPy.algorithms.algorithm.Algorithm.initPopulation`
"""
X, X_f, args, _ = Algorithm.init_population(self, task)
v = np.full([self.NP, task.D], 0.0)
return X, X_f, args, {'v': v}
def init_population(self, task):
r"""Init population.
Args:
task (Task): Optimization task
Returns:
Tuple(numpy.ndarray[MkeSolution], numpy.ndarray, list, dict]:
1. Initialized solutions
2. Fitness/function values of solution
3. Additional arguments
4. Additional keyword arguments
"""
pop, fpop, args, kwargs = Algorithm.init_population(self, task)
for i in self.Rand.choice(self.NP,
int(self.R * len(pop)),
replace=False):
pop[i].MonkeyKing = True
return pop, fpop, args, kwargs
def init_population(self, task):
r"""Initialize the starting population.
Args:
task (Task): Optimization task
Returns:
Tuple[numpy.ndarray, numpy.ndarray, list, dict]:
1. New population.
2. New population fitness/function values.
3. Additional arguments.
4. Additional keyword arguments:
* age (numpy.ndarray): Age of trees.
See Also:
* :func:`WeOptPy.algorithms.Algorithm.initPopulation`
"""
Trees, Evaluations, args, _ = Algorithm.init_population(self, task)
age = np.zeros(self.NP, dtype=np.int32)
self.dx = np.absolute(task.Upper) / 5
return Trees, Evaluations, args, {'age': age}
def init_population(self, task):
r"""Initialize population.
Args:
task (Task): Optimization task.
Returns:
Tuple[numpy.ndarray, numpy.ndarray[float], list, dict]:
1. Initialized population of glowwarms.
2. Initialized populations function/fitness values.
3. Additional arguments.
4. Additional keyword arguments:
* L (numpy.ndarray): TODO.
* R (numpy.ndarray): TODO.
* rs (numpy.ndarray): TODO.
"""
GS, GS_f, args, kwargs = Algorithm.init_population(self, task)
rs = euclidean(np.full(task.D, 0), task.bRange)
L, R = np.full(self.NP, self.l0), np.full(self.NP, rs)
kwargs.update({'L': L, 'R': R, 'rs': rs})
return GS, GS_f, args, kwargs
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 populations function/fitness values.
3. Additional arguments.
4. Additional keyword arguments:
* Ah (numpy.ndarray): Initialized amplitudes.
See Also:
* :func:`NiaPy.algorithms.algorithm.Algorithm.initPopulation`
"""
FW, FW_f, args, kwargs = Algorithm.init_population(self, task)
Ah = self.init_amplitude(task)
kwargs.update({'Ah': Ah})
return FW, FW_f, args, kwargs
def init_population(self, task):
r"""Initialize population.
Args:
task (Task): Optimization task.
Returns:
Tuple[numpy.ndarray, numpy.ndarray[float], Dict[str, Any]]:
1. Initialized population.
2. Initialized populations fitness/function values.
3. Additional arguments:
4. Additional keyword arguments:
* Dictionary of modes (seek or trace) and velocities for each cat
See Also:
* :func:`WeOptPy.algorithms.Algorithm.initPopulation`
"""
pop, fpop, args, d = Algorithm.init_population(self, task)
d['modes'] = self.random_seek_trace()
d['velocities'] = self.uniform(-self.vMax, self.vMax,
[len(pop), task.D])
return pop, fpop, args, d
def init_population(self, task):
r"""Initialize the 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:
* k (int): TODO.
* c (int): TODO.
See Also:
* :func:`NiaPy.algorithms.algorithm.Algorithm.initPopulation`
"""
x, x_f, args, d = Algorithm.init_population(self, task)
k, c = int(ceil(self.NP / task.D)), int(ceil(self.C * task.D))
d.update({'k': k, 'c': c})
return x, x_f, args, d
def init_population(self, task):
r"""Initialize population.
Args:
task (Task): Optimization task.
Returns:
Tuple[numpy.ndarray, numpy.ndarray, list, dict]:
1. Initialized population.
2. Initialized populations fitness/function values.
3. Additional arguments:
4. Additional keyword arguments:
* a (): TODO
See Also:
* :func:`NiaPy.algorithms.Algorithm.initPopulation`
"""
pop, fpop, args, d = Algorithm.init_population(self, task)
si = np.argsort(fpop)
A, A_f, B, B_f, D, D_f = np.copy(pop[si[0]]), fpop[si[0]], np.copy(
pop[si[1]]), fpop[si[1]], np.copy(pop[si[2]]), fpop[si[2]]
d.update({'A': A, 'A_f': A_f, 'B': B, 'B_f': B_f, 'D': D, 'D_f': D_f})
return pop, fpop, args, d