algorithm.run()
front = algorithm.get_result()
# Plot front
plot_front = Plot(
title="Pareto front approximation. Problem: " + problem.get_name(),
reference_front=problem.reference_front,
axis_labels=problem.obj_labels,
)
plot_front.plot(front,
label=algorithm.label,
filename=algorithm.get_name())
# Plot interactive front
plot_front = InteractivePlot(
title="Pareto front approximation. Problem: " + problem.get_name(),
reference_front=problem.reference_front,
axis_labels=problem.obj_labels,
)
plot_front.plot(front,
label=algorithm.label,
filename=algorithm.get_name())
# Save results to file
print_function_values_to_file(front, "FUN." + algorithm.label)
print_variables_to_file(front, "VAR." + algorithm.label)
print(f"Algorithm: {algorithm.get_name()}")
print(f"Problem: {problem.get_name()}")
print(f"Computing time: {algorithm.total_computing_time}")
if __name__ == "__main__":
problem = Rastrigin(10)
max_evaluations = 50000
algorithm = NSGAII(
problem=problem,
population_size=100,
offspring_population_size=100,
mutation=PolynomialMutation(probability=1.0 /
problem.number_of_variables,
distribution_index=20.0),
crossover=SBXCrossover(probability=0.9, distribution_index=20.0),
termination_criterion=StoppingByEvaluations(
max_evaluations=max_evaluations),
dominance_comparator=DominanceComparator(),
)
algorithm.run()
front = algorithm.get_result()
# Save results to file
print_function_values_to_file(
front, "FUN." + algorithm.get_name() + "-" + problem.get_name())
print_variables_to_file(
front, "VAR." + algorithm.get_name() + "-" + problem.get_name())
print("Algorithm (continuous problem): " + algorithm.get_name())
print("Problem: " + problem.get_name())
print("Computing time: " + str(algorithm.total_computing_time))
crossover=SBXCrossover(probability=1.0, distribution_index=20),
termination_criterion=StoppingEvaluator
# termination_criterion = StoppingByQualityIndicator(quality_indicator=HyperVolume, expected_value=1,
# degree=0.9)
# selection = BinaryTournamentSelection()
)
"""==========================调用算法模板进行种群进化========================="""
# progress_bar = ProgressBarObserver(max=max_evaluations)
# algorithm.observable.register(progress_bar)
algorithm.run()
front = algorithm.get_result()
"""==================================输出结果=============================="""
# Save results to file
fun_name = 'FUN.' + str(round_index) + '_' + algorithm.label
print_function_values_to_file(front,
os.path.join(target_dir, fun_name))
var_name = 'VAR.' + str(round_index) + '_' + algorithm.label
print_variables_to_file(front, os.path.join(target_dir, var_name))
print(f'Algorithm: ${algorithm.get_name()}')
print(f'Problem: ${problem.get_name()}')
print(f'Computing time: ${algorithm.total_computing_time}')
# print(search_round,round_index,total_search_round)
search_round = int(StoppingEvaluator.evaluations / population)
total_round = total_round - search_round
print("real round: ", search_round, "idx: ", round_index, "left: ",
total_round)
round_index = round_index + 1
from jmetal.algorithm.singleobjective.simulated_annealing import SimulatedAnnealing
from jmetal.operator import PolynomialMutation
from jmetal.problem.singleobjective.unconstrained import Rastrigin
from jmetal.util.solution import print_function_values_to_file, print_variables_to_file
from jmetal.util.termination_criterion import StoppingByEvaluations
if __name__ == '__main__':
problem = Rastrigin(10)
max_evaluations = 250000
algorithm = SimulatedAnnealing(
problem=problem,
mutation=PolynomialMutation(probability=1.0 / problem.number_of_variables, distribution_index=20.0),
termination_criterion=StoppingByEvaluations(max=max_evaluations)
)
algorithm.run()
result = algorithm.get_result()
# Save results to file
print_function_values_to_file(result, 'FUN.'+ algorithm.get_name() + "." + problem.get_name())
print_variables_to_file(result, 'VAR.' + algorithm.get_name() + "." + problem.get_name())
print('Algorithm: ' + algorithm.get_name())
print('Problem: ' + problem.get_name())
print('Solution: ' + str(result.variables[0]))
print('Fitness: ' + str(result.objectives[0]))
print('Computing time: ' + str(algorithm.total_computing_time))
from jmetal.algorithm.multiobjective import NSGAII
from jmetal.operator import SBXCrossover, PolynomialMutation
from jmetal.problem import ZDT1
from jmetal.util.termination_criterion import StoppingByEvaluations
problem = ZDT1()
algorithm = NSGAII(
problem=problem,
population_size=100,
offspring_population_size=100,
mutation=PolynomialMutation(probability=1.0 / problem.number_of_variables, distribution_index=20),
crossover=SBXCrossover(probability=1.0, distribution_index=20),
termination_criterion=StoppingByEvaluations(max_evaluations=25000)
)
algorithm.run()
from jmetal.util.solution import get_non_dominated_solutions, print_function_values_to_file, \
print_variables_to_file
front = get_non_dominated_solutions(algorithm.get_result())
print_function_values_to_file(front, 'FUN.NSGAII.ZDT1')
print_variables_to_file(front, 'VAR.NSGAII.ZDT1')
from jmetal.lab.visualization import Plot
plot_front = Plot(title='Pareto front approximation', axis_labels=['x', 'y'])
plot_front.plot(front, label='NSGAII-ZDT1', filename='NSGAII-ZDT1', format='png')
mutation=PolynomialMutation(probability=1.0 / problem.number_of_variables, distribution_index=20),
crossover=SBXCrossover(probability=1.0, distribution_index=20),
termination_criterion = StoppingByEvaluations(max_evaluations=max_evaluations)
# termination_criterion = StoppingByQualityIndicator(quality_indicator=HyperVolume, expected_value=1,
# degree=0.9)
# selection = BinaryTournamentSelection()
)
elif Configuration.algorithm == 'Random':
algorithm = RandomSearch(
problem=problem,
termination_criterion=StoppingByEvaluations(max_evaluations=max_evaluations)
)
"""==========================调用算法模板进行种群进化========================="""
progress_bar = ProgressBarObserver(max=max_evaluations)
algorithm.observable.register(progress_bar)
algorithm.run()
front = algorithm.get_result()
"""==================================输出结果=============================="""
# Save results to file
print_function_values_to_file(front, os.path.join(target_dir, '/FUN.' + algorithm.label))
print_variables_to_file(front, os.path.join(target_dir, '/VAR.' + algorithm.label))
print(f'Algorithm: ${algorithm.get_name()}')
print(f'Problem: ${problem.get_name()}')
print(f'Computing time: ${algorithm.total_computing_time}')
population_size=pop_size,
offspring_population_size=int(pop_size * Cr),
mutation=MulticastMutation(probability=Pmut,
Pnmut=Pnmut,
G=gr),
crossover=MulticastCrossover(
probability=Cr,
root_node=root_node,
destination_nodes=destination_nodes,
graph=gr),
termination_criterion=StoppingByEvaluations(
max_evaluations=max_evaluations))
algorithm.run()
front = algorithm.get_result()
# Save results to file
print_function_values_to_file(
front,
f"results/multiobjective/{params['algorithm']}/{params['pop_size']}_{params['nbr_generations']}_{params['network']}_{params['experiment']}_{i}"
)
plot_front = Plot(title='Pareto front approximation',
axis_labels=['cost', 'delay'])
plot_front.plot(front,
label=f'{params["algorithm"]}',
filename=f'{params["algorithm"]}',
format='png')
print(f'Algorithm: ${algorithm.get_name()}')
print(f'Problem: ${problem.get_name()}')
print(f'Computing time: ${algorithm.total_computing_time}')
Configuration = configure()
# global BestPopulation
BestPopulation = BestPop(Configuration)
# config.createfolders()
Goal_num = Configuration.goal_num
# file_name = text_create(Configuration )
# output = sys.stdout
# outputfile = codecs.open(file_name, 'w', 'utf-8')
# sys.stdout = outputfile
"""===============================实例化问题对象============================"""
problem = OvertakeProblem(Goal_num, Configuration, BestPopulation)
"""=================================算法参数设置============================"""
max_evaluations = Configuration.maxIterations
algorithm = RandomSearch(problem=problem,
termination_criterion=StoppingByEvaluations(
max_evaluations=max_evaluations))
algorithm.run()
front = algorithm.get_result()
# Save results to file
print_function_values_to_file(
front, 'FUN.' + Configuration.file_dir_eval + algorithm.label)
print_variables_to_file(
front, 'VAR.' + Configuration.file_dir_var + algorithm.label)
print(f'Algorithm: ${algorithm.get_name()}')
print(f'Problem: ${problem.get_name()}')
print(f'Computing time: ${algorithm.total_computing_time}')