def init(self, task):
r"""Initialize dynamic arguments of Particle Swarm Optimization algorithm.
Args:
task (Task): Optimization task.
Returns:
Dict[str, numpy.ndarray]:
* vMin: Minimal velocity.
* vMax: Maximal velocity.
* V: Initial velocity of particle.
* flag: Refresh gap counter.
"""
return {
'min_velocity':
full_array(self.min_velocity, task.dimension),
'max_velocity':
full_array(self.max_velocity, task.dimension),
'v':
np.full([self.population_size, task.dimension], 0.0),
'flag':
np.full(self.population_size, 0),
'pc':
np.asarray([
.05 + .45 * (np.exp(10 * (i - 1) /
(self.population_size - 1)) - 1) /
(np.exp(10) - 1) for i in range(self.population_size)
])
}
def init_weights(self, task):
r"""Initialize weights.
Args:
task (Task): Optimization task.
Returns:
Tuple[numpy.ndarray, numpy.ndarray]:
1. Weights for neighborhood.
2. Weights for foraging.
"""
return full_array(self.W_n,
task.dimension), full_array(self.W_f, task.dimension)
def init(self, _task):
r"""Initialize dynamic parameters of algorithm.
Args:
_task (Task): Optimization task.
Returns:
Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray]
1. Array of `self.alpha` propagated values
2. Array of `self.gamma` propagated values
3. Array of `self.theta` propagated values
"""
return full_array(self.alpha, self.population_size), full_array(
self.gamma,
self.population_size), full_array(self.theta, self.population_size)
def init(self, task):
r"""Initialize dynamic arguments of Particle Swarm Optimization algorithm.
Args:
task (Task): Optimization task.
Returns:
Dict[str, Union[float, numpy.ndarray]]:
* w (numpy.ndarray): Inertial weight.
* min_velocity (numpy.ndarray): Minimal velocity.
* max_velocity (numpy.ndarray): Maximal velocity.
* v (numpy.ndarray): Initial velocity of particle.
"""
return {
'w': full_array(self.w, task.dimension),
'min_velocity': full_array(self.min_velocity, task.dimension),
'max_velocity': full_array(self.max_velocity, task.dimension),
'v': np.zeros((self.population_size, task.dimension))
}
def test_a_float_array3(self):
a = [25.25 + i for i in range(10)]
arr = full_array(np.asarray(a), 9)
a.remove(34.25)
self.assertTrue(np.array_equal(arr, np.asarray(a)))
def test_a_int_list3(self):
a = [25 + i for i in range(10)]
arr = full_array(a, 9)
a.remove(34)
self.assertTrue(np.array_equal(arr, np.asarray(a)))
def test_a_int_array2(self):
a = [25 + i for i in range(10)]
arr = full_array(np.asarray(a), 13)
a.extend([25 + i for i in range(3)])
self.assertTrue(np.array_equal(arr, np.asarray(a)))
def test_a_float_array1(self):
a = [25.25 + i for i in range(10)]
arr = full_array(np.asarray(a), 15)
a.extend([25.25 + i for i in range(5)])
self.assertTrue(np.array_equal(arr, np.asarray(a)))
def test_a_int_array(self):
a = np.asarray([25] * 10)
arr = full_array(a, 10)
self.assertTrue(np.array_equal(arr, np.full(10, 25)))
def test_a_float_list(self):
a = [25.25] * 10
arr = full_array(a, 10)
self.assertTrue(np.array_equal(arr, np.full(10, 25.25)))
def test_a_int(self):
arr = full_array(25, 10)
self.assertTrue(np.array_equal(arr, np.full(10, 25)))
def setUp(self):
self.D = 10
self.Upper, self.Lower = full_array(10,
self.D), full_array(-10, self.D)
self.met = repair.limit
def generate_individual(dim, upper, lower):
upp, low = full_array(upper, dim), full_array(lower, dim)
return default_rng().uniform(low, upp, dim)
