def init(self, task):
r"""Initialize dynamic arguments of Particle Swarm Optimization algorithm.
Args:
task (Task): Optimization task.
Returns:
Dict[str, numpy.ndarray]:
* vMin: Mininal velocity.
* vMax: Maximal velocity.
* V: Initial velocity of particle.
* flag: Refresh gap counter.
"""
return {
'min_velocity':
full_array(self.vMin, task.D),
'max_velocity':
full_array(self.vMax, task.D),
'v':
np.full([self.NP, task.D], 0.0),
'flag':
np.full(self.NP, 0),
'pc':
np.asarray([
.05 + .45 * (np.exp(10 * (i - 1) / (self.NP - 1)) - 1) /
(np.exp(10) - 1) for i in range(self.NP)
])
}
def test_range(self):
self.assertTrue(
np.array_equal(
full_array(self.Upper, self.D) -
full_array(self.Lower, self.D), self.task.bRange))
self.assertTrue(
np.array_equal(
full_array(self.Upper, self.D) -
full_array(self.Lower, self.D), self.task.range()))
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.NP), full_array(self.gamma, self.NP), full_array(self.theta, self.NP)
def init_ranges(self, task):
r"""Initialize ranges.
Args:
task (Task): Optimization task.
Returns:
Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray]:
1. Initial amplitude values over dimensions.
2. Final amplitude values over dimensions.
3. uAmin.
"""
Ainit, Afinal = full_array(self.Ainit,
task.D), full_array(self.Afinal, task.D)
return Ainit, Afinal, self.uAmin(Ainit, Afinal, task)
def init_amplitude(self, task):
r"""Initialize amplitudes for dimensions.
Args:
task (Task): Optimization task.
Returns:
numpy.ndarray: Starting amplitudes.
"""
return full_array(self.A, task.D)
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.
* vMin (numpy.ndarray): Minimal velocity.
* vMax (numpy.ndarray): Maximal velocity.
* V (numpy.ndarray): Initial velocity of particle.
"""
return {
'w': full_array(self.w, task.D),
'min_velocity': full_array(self.vMin, task.D),
'max_velocity': full_array(self.vMax, task.D),
'v': np.full([self.NP, task.D], 0.0)
}
def test_a_int_array2_fine(self):
a = [25 + i for i in range(10)]
A = full_array(np.asarray(a), 13)
a.extend([25 + i for i in range(3)])
self.assertTrue(np.array_equal(A, np.asarray(a)))
def test_is_feasible(self):
self.assertFalse(self.task.is_feasible(full_array([1, 2, 3], self.D)))
def test_a_int_array3_fine(self):
a = [25 + i for i in range(10)]
A = full_array(np.asarray(a), 9)
a.remove(34)
self.assertTrue(np.array_equal(A, np.asarray(a)))
def test_upper(self):
self.assertTrue(
np.array_equal(full_array(self.Upper, self.D), self.task.Upper))
self.assertTrue(
np.array_equal(full_array(self.Upper, self.D), self.task.upper()))
def test_lower(self):
self.assertTrue(
np.array_equal(full_array(self.Lower, self.D), self.task.Lower))
self.assertTrue(
np.array_equal(full_array(self.Lower, self.D), self.task.lower()))
def setUp(self):
self.D, self.NP, self.F, self.CR, self.p = 10, 100, 0.5, 0.5, 0.25
self.Upper, self.Lower = full_array(100,
self.D), full_array(-100, self.D)
self.evalFun = Sphere().function()
def test_a_float_list3_fine(self):
a = [25.25 + i for i in range(10)]
A = full_array(a, 9)
a.remove(34.25)
self.assertTrue(np.array_equal(A, np.asarray(a)))
def test_a_float_array1_fine(self):
a = [25.25 + i for i in range(10)]
A = full_array(np.asarray(a), 15)
a.extend([25.25 + i for i in range(5)])
self.assertTrue(np.array_equal(A, np.asarray(a)))
def test_a_int_array_fine(self):
a = np.asarray([25 for i in range(10)])
A = full_array(a, 10)
self.assertTrue(np.array_equal(A, np.full(10, 25)))
def generate_individual(self, D, upper, lower):
arr_upper, arr_lower = full_array(upper, D), full_array(lower, D)
return arr_lower + rnd.rand(D) * (arr_upper - arr_lower)
def setUp(self):
self.D = 10
self.Upper, self.Lower = full_array(10,
self.D), full_array(-10, self.D)
self.met = limit_repair
def test_a_float_list_fine(self):
a = [25.25 for i in range(10)]
A = full_array(a, 10)
self.assertTrue(np.array_equal(A, np.full(10, 25.25)))
def test_a_int_fine(self):
A = full_array(25, 10)
self.assertTrue(np.array_equal(A, np.full(10, 25)))
