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=============================="""
file_name = target_dir + '/searched_violation_pattern_' + str(
round_index) + '.txt'
numpy.savetxt(file_name, searched_violation_pattern, fmt="%d") # 保存为整数
file_name = target_dir + '/violation_pattern_ranking_removed_' + str(
round_index) + '.txt'
numpy.savetxt(file_name, violation_pattern_ranking_removed,
fmt="%d") # 保存为整数
# 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)
round_index = round_index + 1
reference_front=problem.reference_front, display_frequency=100))
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('Algorithm (continuous problem): ' + algorithm.get_name())
print('Problem: ' + problem.get_name())
print('Computing time: ' + str(algorithm.total_computing_time))
if __name__ == "__main__":
problem = LZ09_F2()
problem.reference_front = read_solutions(filename="resources/reference_front/LZ09_F2.pf")
max_evaluations = 150000
algorithm = MOEAD(
problem=problem,
population_size=300,
crossover=DifferentialEvolutionCrossover(CR=1.0, F=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_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)
print(f"Algorithm: {algorithm.get_name()}")
print(f"Problem: {problem.get_name()}")
print(f"Computing time: {algorithm.total_computing_time}")
from jmetal.util.termination_criterion import StoppingByEvaluations
if __name__ == "__main__":
problem = Rastrigin(10)
max_evaluations = 100000
algorithm = SimulatedAnnealing(
problem=problem,
mutation=PolynomialMutation(probability=1.0 /
problem.number_of_variables,
distribution_index=20.0),
termination_criterion=StoppingByEvaluations(
max_evaluations=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))
if __name__ == '__main__':
problem = ZDT1Modified()
problem.reference_front = read_solutions(
filename='resources/reference_front/ZDT1.pf')
max_evaluations = 100
algorithm = SMPSO(
problem=problem,
swarm_size=10,
mutation=PolynomialMutation(probability=1.0 /
problem.number_of_variables,
distribution_index=20),
leaders=CrowdingDistanceArchive(10),
termination_criterion=StoppingByEvaluations(max=max_evaluations),
swarm_evaluator=SparkEvaluator(),
)
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))
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}')
