def test_should_is_feasible_return_true_if_the_solution_has_no_constraints(
self) -> None:
solution = Solution(number_of_variables=2,
number_of_objectives=2,
number_of_constraints=0)
self.assertEqual(True, is_feasible(solution))
def update_external_archive(self):
feasible_solutions = []
for solution in self.solutions:
if is_feasible(solution):
feasible_solutions.append(copy.deepcopy(solution))
if len(feasible_solutions) > 0:
feasible_solutions = feasible_solutions + self.archive
ranking = FastNonDominatedRanking()
ranking.compute_ranking(feasible_solutions)
first_rank_solutions = ranking.get_subfront(0)
if len(first_rank_solutions) <= self.population_size:
self.archive = []
for solution in first_rank_solutions:
self.archive.append(copy.deepcopy(solution))
else:
crowding_distance = CrowdingDistance()
while len(first_rank_solutions) > self.population_size:
crowding_distance.compute_density_estimator(
first_rank_solutions)
first_rank_solutions = sorted(
first_rank_solutions,
key=lambda x: x.attributes['crowding_distance'],
reverse=True)
first_rank_solutions.pop()
self.archive = []
for solution in first_rank_solutions:
self.archive.append(copy.deepcopy(solution))
def test_should_is_feasible_return_false_if_the_solution_has_is_not_feasible(
self) -> None:
solution = Solution(number_of_variables=2,
number_of_objectives=2,
number_of_constraints=1)
solution.constraints[0] = -1
self.assertEqual(False, is_feasible(solution))