while total_round > 0:
# for round_index in range (total_round):
if round_index == 0:
goal_index = 91
# goal_index = 110
goal_selection_flag = priority_list[goal_index]
print(goal_selection_flag)
search_round = 100
if total_round < search_round:
search_round = total_round
# total_round = total_round - search_round
Configuration = CarBehindAndInFrontConfigure(
goal_index, population, target_dir, round_index, search_round)
vars_file_name = Configuration.file_dir_var
results_file_name = Configuration.file_dir_eval
searched_violation_pattern.append(goal_index)
else:
# goal_index = 91
goal_index = 110
goal_selection_flag = priority_list[goal_index]
print(goal_selection_flag)
search_round = 100
if total_round < search_round:
search_round = total_round
# total_round = total_round - search_round
Configuration = CarBehindAndInFrontConfigure(
full_path = desktop_path + str(time.strftime("%Y_%m_%d_")) + str(
Configuration.algorithm) + '_iteration.txt' # 也可以创建一个.doc的word文档
file = open(full_path, 'w')
return full_path
data_folder = os.getcwd() + '/Overtake_Datalog_50_1000_' + str(
time.strftime("%Y_%m_%d_%H"))
if not os.path.exists(data_folder):
os.mkdir(data_folder)
if __name__ == '__main__':
target_dir = data_folder
Configuration = CarBehindAndInFrontConfigure(target_dir)
Goal_num = Configuration.goal_num
"""===============================实例化问题对象============================"""
problem = CarBehindAndInFrontProblem(Goal_num, Configuration)
"""=================================算法参数设置============================"""
max_evaluations = Configuration.maxIterations
algorithm = NSGAIII(
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 /
while total_round > 0:
## caculate goal_index
if round_index == 0:
goal_selection_flag = numpy.ones(7)
# goal_selection_flag = [0, 0, 0, 0, 0, 1, 0]
searched_violation_pattern.append(goal_selection_flag)
search_round = search_round_list[int(sum(goal_selection_flag))]
# search_round = 50
if total_round < search_round:
search_round = total_round
# total_round = total_round - search_round
Configuration = CarBehindAndInFrontConfigure(goal_selection_flag, population, search_round, round_index, target_dir)
vars_file_name = Configuration.file_dir_var
results_file_name = Configuration.file_dir_eval
else:
# print(round_index, sum(pattern_count))
fileList = os.listdir(results_file_name)
fileList.sort()
for i in range(population * search_round):
textname = results_file_name + '/' + fileList[i]
# print(textname)
result = numpy.loadtxt(textname)
evaluation.append(result)
goal_flag = numpy.zeros((7), dtype=int)
for j in range(7):
# config = Value('Configure', config)
# bestpop = Value('BestPop', bestpop)
if __name__ == '__main__':
goal_selection_index = random.sample(range(0, 128), 20)
total_round = len(goal_selection_index)
population = 20
search_round = 50
numpy.savetxt('goal_selection_index.txt', goal_selection_index,
fmt="%d") # 保存为整数
# round_idx = 0
for round_idx in range(total_round):
Configuration = CarBehindAndInFrontConfigure(
goal_selection_index[round_idx], population, search_round)
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,