swarm_size = 100
reference_point = [[0.1, 0.8], [0.6, 0.1]]
archives_with_reference_points = []
for point in reference_point:
archives_with_reference_points.append(
CrowdingDistanceArchiveWithReferencePoint(
int(swarm_size / len(reference_point)), point))
max_evaluations = 50000
algorithm = SMPSORP(problem=problem,
swarm_size=swarm_size,
mutation=PolynomialMutation(
probability=1.0 / problem.number_of_variables,
distribution_index=20),
reference_points=reference_point,
leaders=archives_with_reference_points,
termination_criterion=StoppingByEvaluations(
max_evaluations=max_evaluations))
algorithm.observable.register(
observer=VisualizerObserver(reference_front=problem.reference_front,
reference_point=reference_point))
algorithm.run()
front = algorithm.get_result()
# Plot front
plot_front = Plot(title='Pareto front approximation. Problem: ' +
problem.get_name(),