def init_population(self, task):
r"""Initialize starting population.
Args:
task (Task): Optimization task.
Returns:
Tuple[numpy.ndarray, numpy.ndarray, Dict[str, Any]]:
1. Initialized population.
2. Initialized populations function/fitness value.
3. Additional arguments:
* enable (numpy.ndarray): If solution/individual is enabled.
* improve (numpy.ndarray): If solution/individual is improved.
* search_range (numpy.ndarray): Search range.
* grades (numpy.ndarray): Grade of solution/individual.
"""
population, fitness, d = Algorithm.init_population(self, task)
enable = np.full(self.population_size, True)
improve = np.full(self.population_size, True)
search_range = np.full((self.population_size, task.dimension), task.range / 2)
grades = np.zeros(self.population_size)
d.update({
'enable': enable,
'improve': improve,
'search_range': search_range,
'grades': grades
})
return population, fitness, d
def init_population(self, task):
r"""Initialize stating population.
Args:
task (Task): Optimization task.
Returns:
Tuple[numpy.ndarray, numpy.ndarray, Dict[str, Any]]:
1. Initialized population.
2. Initialized populations function/fitness values.
3. Additional arguments:
* w_neighbor (numpy.ndarray): Weights neighborhood.
* w_foraging (numpy.ndarray): Weights foraging.
* induced_speed (numpy.ndarray): Induced speed.
* foraging_speed (numpy.ndarray): Foraging speed.
See Also:
* :func:`niapy.algorithms.algorithm.Algorithm.init_population`
"""
krill_herd, krill_herd_fitness, d = Algorithm.init_population(
self, task)
w_neighbor, w_foraging = self.init_weights(task)
induced_speed, foraging_speed = np.zeros(
self.population_size), np.zeros(self.population_size)
d.update({
'w_neighbor': w_neighbor,
'w_foraging': w_foraging,
'induced_speed': induced_speed,
'foraging_speed': foraging_speed
})
return krill_herd, krill_herd_fitness, d
def test_init_population_individual(self):
r"""Test if custom generation initialization works ok."""
a = Algorithm(population_size=10, initialization_function=init_pop_individual, individual_type=Individual)
t = Task(problem=MyProblem(dimension=20))
i = Individual(x=np.zeros(t.dimension), task=t)
pop, fpop, d = a.init_population(t)
for e in pop:
self.assertEqual(i, e)
def test_init_population_numpy(self):
r"""Test if custom generation initialization works ok."""
a = Algorithm(population_size=10,
initialization_function=init_pop_numpy)
t = Task(problem=MyProblem(dimension=20))
self.assertTrue(
np.array_equal(np.zeros((10, t.dimension)),
a.init_population(t)[0]))
def init_population(self, task):
r"""Init population.
Args:
task (Task): Optimization task
Returns:
Tuple(numpy.ndarray[MkeSolution], numpy.ndarray[float], Dict[str, Any]]:
1. Initialized solutions
2. Fitness/function values of solution
3. Additional arguments
"""
pop, fpop, _ = Algorithm.init_population(self, task)
for i in self.rng.choice(self.population_size, int(self.population_rate * len(pop)), replace=False):
pop[i].monkey_king = True
return pop, fpop, {}
def init_population(self, task):
r"""Initialize the starting population.
Args:
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:
* alpha (float): Step size.
See Also:
* :func:`niapy.algorithms.Algorithm.init_population`
"""
fireflies, intensity, _ = Algorithm.init_population(self, task)
return fireflies, intensity, {'alpha': self.alpha}
def init_population(self, task):
r"""Initialize starting population.
Args:
task (Task): Optimization task.
Returns:
Tuple[numpy.ndarray[Individual], numpy.ndarray[float], Dict[str, Any]]:
1. Initialized population.
2. Initialized populations function/fitness values.
3. Additional arguments:
* ki (int): Number of successful mutations.
See Also:
* :func:`niapy.algorithms.algorithm.Algorithm.init_population`
"""
c, fc, d = Algorithm.init_population(self, task)
d.update({'ki': 0})
return c, fc, d
def init_population(self, task):
r"""Initialize the initial population.
Args:
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:
* velocities (numpy.ndarray[float]): Velocities
See Also:
* :func:`niapy.algorithms.Algorithm.init_population`
"""
population, fitness, d = Algorithm.init_population(self, task)
velocities = np.zeros((self.population_size, task.dimension))
d.update({'velocities': velocities})
return population, fitness, d
def init_population(self, task):
r"""Initialize the starting population.
Args:
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:
* age (numpy.ndarray[int32]): Age of trees.
See Also:
* :func:`niapy.algorithms.Algorithm.init_population`
"""
trees, fitness, _ = Algorithm.init_population(self, task)
age = np.zeros(self.population_size, dtype=np.int32)
self.dx = np.absolute(task.upper) / 5.0
return trees, fitness, {'age': age}
def init_population(self, task):
r"""Initialize first population and additional arguments.
Args:
task (Task): Optimization task
Returns:
Tuple[numpy.ndarray, numpy.ndarray, dict]:
1. Initialized population
2. Initialized population fitness/function values
3. Dict[str, Any]:
* x_best (numpy.ndarray): Initialized populations best positions.
* x_best_fitness (numpy.ndarray): Initialized populations best positions function/fitness values.
* alpha (numpy.ndarray):
* gamma (numpy.ndarray):
* theta (numpy.ndarray):
* rs (float): distance of search space.
See Also:
* :func:`niapy.algorithms.algorithm.Algorithm.init_population`
* :func:`niapy.algorithms.other.aso.AnarchicSocietyOptimization.init`
"""
population, population_fitness, d = Algorithm.init_population(
self, task)
alpha, gamma, theta = self.init(task)
x_best, x_best_fitness = self.update_personal_best(
population, task.optimization_type.value * population_fitness,
np.zeros((self.population_size, task.dimension)),
np.full(self.population_size, np.inf))
d.update({
'x_best': x_best,
'x_best_fitness': x_best_fitness,
'alpha': alpha,
'gamma': gamma,
'theta': theta,
'rs': self.d(task.upper, task.lower)
})
return population, population_fitness, d
def init_population(self, task):
r"""Initialize the population.
Args:
task (Task): Optimization task.
Returns:
Tuple[numpy.ndarray, numpy.ndarray[float], Dict[str, Any]]:
1. Initialized population.
2. Initialized population function/fitness values.
3. Additional arguments:
* k (int): Starting number of rows to include from lower triangular matrix.
* c (int): Constant.
See Also:
* :func:`niapy.algorithms.algorithm.Algorithm.init_population`
"""
population, fitness, d = Algorithm.init_population(self, task)
k, c = int(ceil(self.population_size / task.dimension)), int(ceil(self.c * task.dimension))
d.update({'k': k, 'c': c})
return population, fitness, d
