# crossover=SBXCrossover(probability=1, distribution_index=20),
# # selection=RouletteWheelSelection(),
# termination_criterion=StoppingEvaluator
# # termination_criterion = StoppingByQualityIndicator(quality_indicator=HyperVolume, expected_value=1,
# # degree=0.9)
# # selection = BinaryTournamentSelection()
# )
algorithm = NSGAII(
target_pattern=goal_selection_flag,
target_value_threshold=target_values,
problem=problem,
population_size=Configuration.population,
offspring_population_size=Configuration.population,
population_evaluator=MultiprocessEvaluator(
Configuration.ProcessNum),
# population_evaluator=SequentialEvaluator(),
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))
"""==========================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()
# Save results to file
fun_name = 'FUN.' + str(round_index) + '_' + algorithm.label
Goal_num = Configuration.goal_num
"""===============================实例化问题对象============================"""
problem = CarBehindAndInFront(Goal_num, Configuration)
"""=================================算法参数设置============================"""
max_evaluations = population * search_round
# print(max_evaluations)
if Configuration.algorithm == "NSGA_II":
algorithm = NSGAII(
population_evaluator=MultiprocessEvaluator(
Configuration.ProcessNum),
# population_evaluator=SequentialEvaluator(),
problem=problem,
population_size=Configuration.population,
offspring_population_size=Configuration.population,
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=FitnessValue, expected_value=1, degree=0.9)
# selection = BinaryTournamentSelection()
)
elif Configuration.algorithm == "NSGA_III" or Configuration.algorithm == "Brute_Froce":
algorithm = NSGAIII(
population_evaluator=MultiprocessEvaluator(
Configuration.ProcessNum),
# population_evaluator=SequentialEvaluator(),
problem=problem,
population_size=Configuration.population,
reference_directions=UniformReferenceDirectionFactory(