def init_population(self, task):
r"""Initialize the starting population.
Parameters:
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:
* a (float): Loudness.
* S (numpy.ndarray): TODO
* Q (numpy.ndarray): TODO
* v (numpy.ndarray): TODO
See Also:
* :func:`WeOptPy.algorithms.Algorithm.initPopulation`
"""
sol, fitness, args, d = Algorithm.init_population(self, task)
a, s, Q, v = np.full(self.NP,
self.A), np.full([self.NP, task.D], 0.0), np.full(
self.NP, 0.0), np.full([self.NP, task.D], 0.0)
d.update({'a': a, 'S': s, 'Q': Q, 'v': v})
return sol, fitness, args, d
def test_init_population_numpy_fine(self):
r"""Test if custome generation initialization works ok."""
a = Algorithm(n=10, init_pop_func=init_pop_numpy)
t = Task(d=20, benchmark=Sphere())
self.assertTrue(
np.array_equal(np.full((10, t.D), 0.0),
a.init_population(t)[0]))
def init_population(self, task):
r"""Initialize first population and additional arguments.
Args:
task (Task): Optimization task
Returns:
Tuple[numpy.ndarray, numpy.ndarray, list, dict]:
1. Initialized population
2. Initialized population fitness/function values
3. Additional arguments.
4. Additional keyword arguments:
* Xpb (numpy.ndarray): Initialized populations best positions.
* Xpb_f (numpy.ndarray): Initialized populations best positions function/fitness values.
* alpha (numpy.ndarray): TODO.
* gamma (numpy.ndarray): TODO.
* theta (numpy.ndarray): TODO.
* rs (float): Distance of search space.
See Also:
* :func:`WeOptPy.algorithms.Algorithm.initPopulation`
* :func:`WeOptPy.algorithms.AnarchicSocietyOptimization.init`
"""
X, X_f, args, d = Algorithm.init_population(self, task)
alpha, gamma, theta = self.init(task)
Xpb, Xpb_f = self.update_best_and_personal_best(X, X_f, np.full([self.NP, task.D], 0.0), np.full(self.NP, task.optType.value * np.inf))
d.update({'Xpb': Xpb, 'Xpb_f': Xpb_f, 'alpha': alpha, 'gamma': gamma, 'theta': theta, 'rs': euclidean(task.Upper, task.Lower)})
return X, X_f, args, d
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 value.
3. Additional arguments:
4. Additional keyword arguments:
* enable (numpy.ndarray): If solution/individual is enabled.
* improve (numpy.ndarray): If solution/individual is improved.
* SR (numpy.ndarray): Search range.
* grades (numpy.ndarray): Grade of solution/individual.
"""
X, X_f, args, d = Algorithm.init_population(self, task)
enable, improve, SR, grades = np.full(self.NP, True), np.full(
self.NP, True), np.full([self.NP, task.D],
task.bRange / 2), np.full(self.NP, 0.0)
d.update({
'enable': enable,
'improve': improve,
'SR': SR,
'grades': grades
})
return X, X_f, args, d
def init_population(self, task):
r"""Initialize the starting population.
Parameters:
task (Task): Optimization task
Returns:
Tuple[numpy.ndarray, numpy.ndarray[float], Dict[str, Any]]:
1. New population.
2. New population fitness/function values.
3. Additional arguments.
4. Additional keyword arguments:
* S (numpy.ndarray): TODO
* Q (numpy.ndarray): TODO
* v (numpy.ndarray): TODO
See Also:
* :func:`WeOptPy.algorithms.Algorithm.initPopulation`
"""
Sol, Fitness, args, d = Algorithm.init_population(self, task)
S, Q, v = np.full([self.NP, task.D],
0.0), np.full(self.NP,
0.0), np.full([self.NP, task.D], 0.0)
d.update({'S': S, 'Q': Q, 'v': v})
return Sol, Fitness, args, d
def test_init_population_individual_fine(self):
r"""Test if custom generation initialization works ok."""
a = Algorithm(n=10,
init_pop_func=init_pop_individual,
itype=Individual)
t = Task(d=20, benchmark=Sphere())
i = Individual(x=np.full(t.D, 0.0), task=t)
pop, fpop, args, kwargs = a.init_population(t)
for e in pop:
self.assertEqual(i, e)
def init_population(self, task):
r"""Initialize the individuals.
Args:
task (Task): Optimization task
Returns:
Tuple[numpy.ndarray, numpy.ndarray, list, dict]:
1. Initialized population of individuals
2. Function/fitness values for individuals
3. Additional arguments
4. Additional keyword arguments
"""
return Algorithm.init_population(self, task)
def init_population(self, task):
r"""Initialize the initial population.
Args:
task (Task): Optimization task.
Returns:
Tuple[numpy.ndarray, numpy.ndarray, list, dict]:
1. Initial population.
2. Initials population fitness/utility function values.
3. Additional arguments.
4. Additional keyword arguments.
"""
pop, fpop, args, d = Algorithm.init_population(self, task)
d.update({'S': np.zeros((self.NP, task.D))})
return pop, fpop, args, d
def init_population(self, task):
r"""Initialize the starting population.
Args:
task (Task): Optimization task
Returns:
Tuple[numpy.ndarray, numpy.ndarray[float], list, dict]:
1. New population.
2. New population fitness/function values.
3. Additional arguments.
4. Additional keyword arguments:
* alpha (float): TODO
See Also:
* :func:`WeOptPy.algorithms.Algorithm.initPopulation`
"""
Fireflies, Intensity, args, _ = Algorithm.init_population(self, task)
return Fireflies, Intensity, args, {'alpha': self.alpha}
def init_population(self, task):
r"""Initialize starting population.
Args:
task (Task): Optimization task.
Returns:
Tuple[numpy.ndarray, numpy.ndarray, list, dict]:
1. Initialized populaiton.
2. Initialized populations function/fitness values.
3. Additional arguments.
4. Additional keyword arguments:
* ki (int): Number of successful mutations.
See Also:
* :func:`WeOptPy.algorithms.Algorithm.initPopulation`
"""
c, fc, args, d = Algorithm.init_population(self, task)
d.update({'ki': 0})
return c, fc, args, d
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 fitness/function values.
3. Additional arguments:
4. Additional keyword arguments:
* pa_v (float): TODO
See Also:
* :func:`WeOptPy.algorithms.Algorithm.initPopulation`
"""
N, N_f, args, d = Algorithm.init_population(self, task)
d.update({'pa_v': self.NP * self.pa})
return N, N_f, args, d
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:
* Probes (numpy.ndarray): TODO
* Trial (numpy.ndarray): TODO
See Also:
* :func:`WeOptPy.algorithms.Algorithm.initPopulation`
"""
foods, fpop, args, _ = Algorithm.init_population(self, task)
probs, trial = np.full(self.FoodNumber,
0.0), np.full(self.FoodNumber, 0.0)
return foods, fpop, args, {'probs': probs, 'trial': trial}
