def test_should_feasibility_ratio_return_zero_if_all_the_solutions_in_a_list_are_unfeasible(
self) -> None:
solution1 = Solution(2, 2, 2)
solution2 = Solution(2, 2, 2)
solution1.constraints[0] = 0
solution1.constraints[1] = -1
solution2.constraints[0] = -2
solution2.constraints[1] = 0
self.assertEqual(0, feasibility_ratio([solution1, solution2]))
def test_should_feasibility_ratio_return_the_right_percentage_of_feasible_solutions(
self) -> None:
solution1 = Solution(2, 2, 1)
solution2 = Solution(2, 2, 1)
solution3 = Solution(2, 2, 1)
solution1.constraints[0] = -1
solution2.constraints[0] = 0
solution3.constraints[0] = -2
self.assertEqual(1 / 3,
feasibility_ratio([solution1, solution2, solution3]))
def update_progress(self) -> None:
super().update_progress()
if self.evaluations % self.population_size == 0:
self.update_external_archive()
self.generation_counter += 1
self.rk = feasibility_ratio(self.solutions)
if self.generation_counter >= self.tc:
self.epsilon_k = 0
else:
if self.rk < 0.95:
self.epsilon_k = (1 - self.tao) * self.epsilon_k
else:
self.epsilon_k = self.phi_max * (1 + self.tao)
def init_progress(self) -> None:
super().init_progress()
# for i in range(self.population_size):
# self.constraints[i] = get_overall_constraint_violation_degree(self.permutation[i])
self.constraints = [overall_constraint_violation_degree(self.solutions[i])
for i in range(0, self.population_size)]
sorted(self.constraints)
self.epsilon_zero = abs(self.constraints[int(ceil(0.05 * self.population_size))])
if self.phi_max < abs(self.constraints[0]):
self.phi_max = abs(self.constraints[0])
self.rk = feasibility_ratio(self.solutions)
self.epsilon_k = self.epsilon_zero
def test_should_feasibility_ratio_raise_and_exception_if_the_solution_list_is_empty(
self) -> None:
with self.assertRaises(Exception):
feasibility_ratio([])