-
Notifications
You must be signed in to change notification settings - Fork 2
/
main.py
123 lines (112 loc) · 5.88 KB
/
main.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
from task import *
import copy
import numpy as np
import os
def start(Current_solution: Platform):
c_iterx = 0
temp = 100
init_solution(Current_solution) # 初始化~
New_solution = copy.deepcopy(Current_solution)
Best_solution = copy.deepcopy(Current_solution)
# print(Current_solution.target)
# print(Current_solution.solution)
# print("profits:{}".format(profits(Best_solution)))
while c_iterx < iterxMax: # 终止条件：达到迭代次数，不满足终止条件就缓慢降低温度继续搜索
while temp > 10:
destroy_index = select_destroy(New_solution)
repair_index = select_repair(New_solution)
Current_solution, New_solution, Best_solution = update(Current_solution, New_solution, Best_solution,
destroy_index, repair_index)
# print("C_profits:{}".format(profits(Current_solution)))
# print("N_profits:{}".format(profits(New_solution)))
# print("Best_profits:{}".format(profits(Best_solution)))
# print(destroy_score)
# print(repair_score)
# print(wDestroy)
# print(wRepair)
temp = Pa * temp # 温度指数下降
iterx_prof.append(profits(Current_solution))
Best_iterx_prof.append(profits(Best_solution))
c_iterx += 1 # 完成一次降温过程算一次迭代
temp = 100 # 完成一次降温过程算一次迭代
print('最优解的利润:{}'.format(profits(Best_solution)))
return profits(Best_solution), Best_solution.solution, Best_solution.target
def text_save(filename, data): # filename为写入CSV文件的路径，data为要写入数据列表.
file = open(filename, 'w')
for item in range(len(data)):
s = str(data[item]).replace('[', '').replace(']', '') # 去除[],这两行按数据不同，可以选择
s = s.replace("'", '').replace(',', '') + '\n' # 去除单引号，逗号，每行末尾追加换行符
file.write(s)
file.close()
# print("{}保存成功".format(filename))
def save_solution(path, solu: list):
sol_path = path + '\\solution.txt'
text_save(sol_path, solu)
path_1 = '.\\data\\EXP_50_1200_1' # 总的实验数据的保存位置 任务数量_时间窗口长度_序号
exp_num = 50 # 种子数
repeat = 10 # 每个种子重复次数
profit = [[0. for _ in range(repeat + 1)] for _ in range(exp_num)]
if __name__ == '__main__':
if not os.path.exists(path_1):
os.makedirs(path_1)
for i in range(exp_num): # 执行50组不同的任务序列 种子 100~149
random_seed = 100 + i
path_2 = '{}\\{}'.format(path_1, random_seed)
if not os.path.exists(path_2):
os.makedirs(path_2)
solution_list = []
task_num = []
for j in range(repeat): # 每个种子 重复执行 repeat 次
print("{}-{}".format(i, j))
solution = Platform(num_schedule, num_task, num_task_info, random_seed)
solution.produce_schedule()
solution.produce_tasks(num_task, num_task_info)
pro, sol, target = start(solution)
task_num.append(len(sol))
profit[i][j] = pro
solution_list.append(sol)
if not os.path.exists(path_2 + '\\available_time'):
os.makedirs(path_2 + '\\available_time')
text_save(path_2 + '\\available_time\\available_time_{}.txt'.format(j), target)
print("结束后的权重{}".format(wDestroy))
del solution
# 重置权重
reset_weight()
task_num.append(sum(task_num)/repeat)
text_save(path_2 + '\\task_num.txt', task_num) # 在文件中保存任务数量
save_solution(path_2, solution_list) # 在文件中保存 解（任务序列）
for i in range(len(profit)):
profit[i][-1] = sum(profit[i]) / repeat
# print(profit)
text_save(path_1 + '\\profit_data.txt', profit)
# init_solution(Current_solution) # 初始化~
# New_solution = copy.deepcopy(Current_solution)
# Best_solution = copy.deepcopy(Current_solution)
# print(Current_solution.target)
# print(Current_solution.solution)
# print("profits:{}".format(profits(Best_solution)))
# while iterx < iterxMax: # 终止条件：达到迭代次数，不满足终止条件就缓慢降低温度继续搜索
# while T > 10:
# destroy_index = select_destroy(New_solution)
# repair_index = select_repair(New_solution)
# Current_solution, New_solution, Best_solution = update(Current_solution, New_solution, Best_solution,
# destroy_index, repair_index)
# print("C_profits:{}".format(profits(Current_solution)))
# print("N_profits:{}".format(profits(New_solution)))
# print("Best_profits:{}".format(profits(Best_solution)))
# print(destroy_score)
# print(repair_score)
# print(wDestroy)
# print(wRepair)
# T = Pa * T # 温度指数下降
# iterx_prof.append(profits(Current_solution))
# Best_iterx_prof.append(profits(Best_solution))
# iterx += 1 # 完成一次降温过程算一次迭代
# T = 100 # 完成一次降温过程算一次迭代
# print("最优解为：{}".format(Best_solution.solution))
# print("任务数：{} 利润为：{} 平均利润：{}".format(len(Best_solution.solution), profits(Best_solution),
# profits(Best_solution) / len(Best_solution.solution)))
# print("最优解的时间窗口：{}".format(Best_solution.target))
# print("最优解的候选任务：(未安排的任务){}".format(Best_solution.list_f))
# print("性价比：{}".format(performance(Best_solution, Best_solution.solution)))
# picture_profit()