def run_optimization(max_eval, num_nodes, pop_size, offspring, trial_name, year):
os_fold = os_sep()
if not os.path.exists('results' + os_fold + year):
os.makedirs('results' + os_fold + year)
multiprocessing.freeze_support()
problem = cequal()
max_evaluations = max_eval
algorithm = NSGAII(
population_evaluator=MultiprocessEvaluator(num_nodes),
problem=problem,
population_size=pop_size,
offspring_population_size=offspring,
mutation=PolynomialMutation(probability= 0.2, distribution_index=20),
crossover=SBXCrossover(probability=0.8, distribution_index=20),
termination_criterion=StoppingByEvaluations(max_evaluations=max_evaluations),
dominance_comparator=DominanceComparator()
)
algorithm.observable.register(ProgressBarObserver(max=max_evaluations))
algorithm.observable.register(observer=BasicObserver())
algorithm.run()
print(os.getcwd())
front = algorithm.get_result()
print('Algorithm (continuous problem): ' + algorithm.get_name())
print('Problem: ' + problem.get_name())
print('Computing time: ' + str(algorithm.total_computing_time/3600))
print_function_values_to_file(front, 'results' + os_fold + year + os_fold + 'OBJ_' + algorithm.get_name() + "_" + trial_name + '.txt')
print_variables_to_file(front, 'results' + os_fold + year + os_fold + 'VAR_' + algorithm.get_name() + "_" + trial_name + '.txt')
numpy.savetxt(file_name, violation_pattern_to_search,
fmt="%d") # 保存为整数
file_name = target_dir + '/pattern_count_' + str(round_index) + '.txt'
numpy.savetxt(file_name, pattern_count, fmt="%d") # 保存为整数
"""===============================实例化问题对象============================"""
problem = CarBehindAndInFrontProblem(Goal_num, Configuration)
"""=================================算法参数设置============================"""
max_evaluations = Configuration.maxIterations
# StoppingEvaluator = StoppingByEvaluations(max_evaluations=max_evaluations, problem=problem)
StoppingEvaluator = StoppingByEvaluations(
max_evaluations=max_evaluations)
algorithm = NSGAIII(
target_pattern=goal_selection_flag,
target_value_threshold=target_values,
population_evaluator=MultiprocessEvaluator(
Configuration.ProcessNum),
# population_evaluator=SequentialEvaluator(),
problem=problem,
population_size=Configuration.population,
reference_directions=UniformReferenceDirectionFactory(
Configuration.goal_num, n_points=Configuration.population - 1),
# offspring_population_size = Configuration.population,
mutation=PolynomialMutation(probability=1.0 /
problem.number_of_variables,
distribution_index=20),
crossover=SBXCrossover(probability=0.4, distribution_index=20),
# selection=RouletteWheelSelection(),
termination_criterion=StoppingEvaluator
# termination_criterion = StoppingByQualityIndicator(quality_indicator=HyperVolume, expected_value=1,
# degree=0.9)
# selection = BinaryTournamentSelection()
def configure_experiment(problems: dict, n_run: int):
"""Configures the experiments
Args:
problems (dict): The MEWpy optimization problem
n_run (int): the number of runs of each MOEA
Returns:
a list of jobs
"""
from mewpy.optimization.jmetal.operators import UniformCrossoverOU, GrowMutationOU, ShrinkMutation, \
SingleMutationOU, MutationContainer
crossover = UniformCrossoverOU(0.5, max_size=candidate_max_size)
mutators = []
mutators.append(GrowMutationOU(1.0, max_size=candidate_max_size))
mutators.append(ShrinkMutation(1.0, min_size=candidate_max_size))
mutators.append(SingleMutationOU(1.0))
mutation = MutationContainer(0.3, mutators=mutators)
jobs = []
for run in range(n_run):
for problem_tag, problem in problems.items():
jobs.append(
Job(
algorithm=NSGAII(
problem=problem,
population_evaluator=MultiprocessEvaluator(N_CPU),
population_size=100,
offspring_population_size=100,
mutation=mutation,
crossover=crossover,
termination_criterion=StoppingByEvaluations(
max_evaluations=max_evaluations)),
algorithm_tag='NSGAII',
problem_tag=problem_tag,
run=run,
))
jobs.append(
Job(algorithm=SPEA2(
problem=problem,
population_evaluator=MultiprocessEvaluator(N_CPU),
population_size=100,
offspring_population_size=100,
mutation=mutation,
crossover=crossover,
termination_criterion=StoppingByEvaluations(
max_evaluations=max_evaluations)),
algorithm_tag='SPEA2',
problem_tag=problem_tag,
run=run))
jobs.append(
Job(
algorithm=IBEA(
problem=problem,
population_evaluator=MultiprocessEvaluator(N_CPU),
kappa=1.,
population_size=100,
offspring_population_size=100,
mutation=mutation,
crossover=crossover,
termination_criterion=StoppingByEvaluations(
max_evaluations=max_evaluations)),
algorithm_tag='IBEA',
problem_tag=problem_tag,
run=run,
))
return jobs
from jmetal.algorithm.multiobjective.nsgaii import NSGAII
from jmetal.operator import SBXCrossover, PolynomialMutation
from jmetal.problem.multiobjective.zdt import ZDT1Modified
from jmetal.util.evaluator import MultiprocessEvaluator
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 = ZDT1Modified()
max_evaluations = 100
algorithm = NSGAII(population_evaluator=MultiprocessEvaluator(8),
problem=problem,
population_size=10,
offspring_population_size=10,
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))
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)
from jmetal.algorithm.multiobjective.nsgaii import NSGAII
from jmetal.operator import PolynomialMutation, SBXCrossover
from jmetal.problem.multiobjective.zdt import ZDT1Modified
from jmetal.util.evaluator import MultiprocessEvaluator
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 = ZDT1Modified()
max_evaluations = 100
algorithm = NSGAII(
population_evaluator=MultiprocessEvaluator(8),
problem=problem,
population_size=10,
offspring_population_size=10,
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),
)
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)
def configure_experiment(problems: dict, n_run: int):
jobs = []
num_processes = config.num_cpu
population = 10
generations = 10
max_evaluations = population * generations
for run in range(n_run):
for problem_tag, problem in problems.items():
reference_point = FloatSolution([0, 0], [1, 1], problem.number_of_objectives, )
reference_point.objectives = np.repeat(1, problem.number_of_objectives).tolist()
jobs.append(
Job(
algorithm=NSGAIII(
problem=problem,
population_size=population,
mutation=PolynomialMutation(probability=1.0 / problem.number_of_variables,
distribution_index=20),
crossover=SBXCrossover(probability=1.0, distribution_index=20),
population_evaluator=MultiprocessEvaluator(processes=num_processes),
termination_criterion=StoppingByEvaluations(max_evaluations=max_evaluations),
reference_directions=UniformReferenceDirectionFactory(4, n_points=100),
),
algorithm_tag='NSGAIII',
problem_tag=problem_tag,
run=run,
)
)
jobs.append(
Job(
algorithm=GDE3(
problem=problem,
population_size=population,
population_evaluator=MultiprocessEvaluator(processes=num_processes),
termination_criterion=StoppingByEvaluations(max_evaluations=max_evaluations),
cr=0.5,
f=0.5,
),
algorithm_tag='GDE3',
problem_tag=problem_tag,
run=run,
)
)
jobs.append(
Job(
algorithm=SPEA2(
problem=problem,
population_size=population,
mutation=PolynomialMutation(probability=1.0 / problem.number_of_variables,
distribution_index=20),
crossover=SBXCrossover(probability=1.0, distribution_index=20),
population_evaluator=MultiprocessEvaluator(processes=num_processes),
termination_criterion=StoppingByEvaluations(max_evaluations=max_evaluations),
offspring_population_size=population,
),
algorithm_tag='SPEA2',
problem_tag=problem_tag,
run=run,
)
)
return jobs