Pmut: float = 0.05
generations: int = 50
pop_size: int = 30
Cr: float = 0.5
max_evaluations: float = generations * pop_size
problem: MulticastProblem = MulticastProblem(
Graph=gr, root_node=root_node, destination_nodes=destination_nodes)
algorithm = MOEAD(problem=problem,
population_size=pop_size,
mutation=MulticastMutation(probability=Pmut,
Pnmut=Pnmut,
G=gr),
crossover=MulticastCrossover(
probability=0.5,
root_node=root_node,
destination_nodes=destination_nodes,
graph=gr),
aggregative_function=Tschebycheff(
dimension=problem.number_of_objectives),
neighbor_size=20,
neighbourhood_selection_probability=0.9,
max_number_of_replaced_solutions=1,
weight_files_path='resources/MOEAD_weights',
termination_criterion=StoppingByEvaluations(
max_evaluations=max_evaluations))
algorithm.run()
front = algorithm.get_result()
# Save results to file
print_function_values_to_file(front, 'FUN.' + algorithm.label)
# print_variables_to_file(front, 'VAR.'+ algorithm.label)
if __name__ == '__main__':
problem = DTLZ2()
problem.reference_front = read_solutions(
filename='../../resources/reference_front/DTLZ2.3D.pf'.format(
problem.get_name()))
max_evaluations = 150000
algorithm = MOEAD(
problem=problem,
population_size=300,
crossover=DifferentialEvolutionCrossover(CR=1.0, F=0.5, K=0.5),
mutation=PolynomialMutation(probability=1.0 /
problem.number_of_variables,
distribution_index=20),
aggregative_function=Tschebycheff(
dimension=problem.number_of_objectives),
neighbor_size=20,
neighbourhood_selection_probability=0.9,
max_number_of_replaced_solutions=2,
weight_files_path='../../resources/MOEAD_weights',
termination_criterion=StoppingByEvaluations(max=max_evaluations))
algorithm.observable.register(observer=ProgressBarObserver(
max=max_evaluations))
algorithm.observable.register(observer=VisualizerObserver(
reference_front=problem.reference_front, display_frequency=1000))
algorithm.run()
front = algorithm.get_result()
from jmetal.algorithm.multiobjective.moead import MOEAD
from jmetal.operator import Polynomial, DifferentialEvolution
from jmetal.problem import LZ09_F2
from jmetal.util.aggregative_function import Chebyshev
from jmetal.util.neighborhood import WeightVectorNeighborhood
from jmetal.util.solution_list import read_front
problem = LZ09_F2()
problem.reference_front = read_front(
file_path='/resources/reference_front/{}.pf'.format(problem.get_name()))
population_size = 100
algorithm = MOEAD(
problem=problem,
population_size=population_size,
max_evaluations=150000,
crossover=DifferentialEvolution(CR=1.0, F=0.5, K=0.5),
mutation=Polynomial(probability=1.0 / problem.number_of_variables,
distribution_index=20),
aggregative_function=Chebyshev(dimension=problem.number_of_objectives),
neighbourhood=WeightVectorNeighborhood(
population_size, 20, weights_path='/resources/MOEAD_weights/'),
neighbourhood_selection_probability=0.9,
max_number_of_replaced_solutions=2)
progress_bar = ProgressBarObserver(initial=algorithm.population_size,
step=algorithm.offspring_size,
maximum=algorithm.max_evaluations)
algorithm.observable.register(observer=progress_bar)