def clMountain(dis_cus_cus, dis_sta_cus, min_dis_depot, min_dis_sta, time_win,
tolerate_time_win, demand, line):
PSCS = 5 # 爬山算法迭代次数
oldFit = calFitness.fitness(dis_cus_cus, dis_sta_cus, min_dis_depot,
min_dis_sta, time_win, tolerate_time_win,
demand, line, False)
i = 0
while i < PSCS:
f = random.uniform(0, len(line))
n = random.uniform(0, len(line))
mutate.doChange(line, f, n)
newFit = calFitness.fitness(dis_cus_cus, dis_sta_cus, min_dis_depot,
min_dis_sta, time_win, tolerate_time_win,
demand, line, False)
if newFit < oldFit:
mutate.doChange(line, f, n)
i += 1
def main():
depots, stations, customers, demand, time_win, tolerate_time_win = readfile.readfile("4depot_10station_40customer3.csv", depot_num=4, station_num=10, customer_num=40)
dis_dep_cus = readfile.get_dis_matrix(depots, customers) #depot到customer的距离矩阵
dis_sta_cus = readfile.get_dis_matrix(stations, customers) #station到customer的距离矩阵
dis_cus_cus = readfile.get_dis_matrix(customers, customers) #customer之间的距离矩阵
min_dis_depot = readfile.get_min_dis(dis_dep_cus) #获取距离每个customer最近的depot及距离
min_dis_station = readfile.get_min_dis(dis_sta_cus) #获取距离每个customer最近的station及距离
mans = 40
rows = 100
times = 300
line = [3, 29, 14, 39, 19, 7, 35, 5, 21, 6, 33, 4, 26, 31, 11, 18, 20, 1, 30, 36, 15, 24, 23, 32, 34, 40, 37, 9, 22, 16, 8, 38, 12, 13, 10, 28, 2, 17, 25, 27]
result = calFitness.fitness(dis_cus_cus, dis_sta_cus, min_dis_depot, min_dis_station, time_win, tolerate_time_win, demand, line, True)
print result
def run(self):
print "开始迭代"
# 路线数组
lines = [[0 for i in range(self.mans)] for i in range(self.rows)]
# 适应度
fit = [0 for i in range(self.rows)]
# print "初始输入获取rows个随机排列，并且计算适应度"
# 初始输入获取rows个随机排列，并且计算适应度
for i in range(0, self.rows):
random.shuffle(self.customers)
lines[i] = self.customers
# 计算每个线路的适应度
# print "计算每个线路的适应度 i = %d" % (i)
fit[i] = calFitness.fitness(self.dep_num, self.dis_cus_cus,
self.dis_sta_cus, self.dis_dep_cus,
self.min_dis_station, self.time_win,
self.tolerate_time_win, self.demand,
lines[i], False)
# 迭代次数
t = 0
while t < self.times:
# 适应度
newLines = [[0 for i in range(self.mans)]
for i in range(self.rows)]
nextFit = [0 for i in range(self.rows)]
randomFit = [0 for i in range(self.rows)]
totalFit = 0
tmpFit = 0
# print "计算总的适应度"
# 计算总的适应度
for i in range(self.rows):
totalFit += fit[i]
# print "通过适应度占总适应度的比例生成随机适应度"
# 通过适应度占总适应度的比例生成随机适应度
for i in range(self.rows):
randomFit[i] = tmpFit + fit[i] / totalFit
tmpFit += randomFit[i]
# print "上一代中的最优直接遗传到下一代"
# 上一代中的最优直接遗传到下一代
m = fit[0]
ml = 0
for i in range(self.rows):
if m < fit[i]:
m = fit[i]
ml = i
for i in range(self.mans):
newLines[0][i] = lines[ml][i]
nextFit[0] = fit[ml]
# print "对最优解使用爬山算法促使其自我进化"
# 对最优解使用爬山算法促使其自我进化
clMountain.clMountain(self.dep_num, self.dis_cus_cus,
self.dis_sta_cus, self.dis_dep_cus,
self.min_dis_station, self.time_win,
self.tolerate_time_win, self.demand,
newLines[0])
# print "开始遗传"
# 开始遗传
nl = 1
while nl < self.rows:
# 选择操作
r = int(randomSelect.randomSelect(randomFit))
# 交叉操作
# 判断是否需要交叉，不能越界
if random.random() < self.JCL and nl + 1 < self.rows:
# 获取交叉排列
rn = int(randomSelect.randomSelect(randomFit))
fline, nline = intercross.intercross(lines[r], lines[rn])
# 基因变异
mutate.change(fline)
mutate.change(nline)
# print "交叉并且变异后的结果加入下一代"
# 交叉并且变异后的结果加入下一代
for i in range(self.mans):
newLines[nl][i] = fline[i]
newLines[nl + 1][i] = nline[i]
nextFit[nl] = calFitness.fitness(
self.dep_num, self.dis_cus_cus, self.dis_sta_cus,
self.dis_dep_cus, self.min_dis_station, self.time_win,
self.tolerate_time_win, self.demand, fline, False)
nextFit[nl + 1] = calFitness.fitness(
self.dep_num, self.dis_cus_cus, self.dis_sta_cus,
self.dis_dep_cus, self.min_dis_station, self.time_win,
self.tolerate_time_win, self.demand, nline, False)
nl += 2
else:
# print "不需要交叉的，直接变异，然后遗传到下一代"
# 不需要交叉的，直接变异，然后遗传到下一代
line = [0 for i in range(self.mans)]
i = 0
while i < self.mans:
line[i] = lines[r][i]
i += 1
# 基因变异
mutate.change(line)
# 加入下一代
i = 0
while i < self.mans:
newLines[nl][i] = line[i]
i += 1
nextFit[nl] = calFitness.fitness(
self.dep_num, self.dis_cus_cus, self.dis_sta_cus,
self.dis_dep_cus, self.min_dis_station, self.time_win,
self.tolerate_time_win, self.demand, line, False)
nl += 1
# print "新的一代覆盖上一代 当前是第 %d 代" %(t)
# 新的一代覆盖上一代
for i in range(self.rows):
for h in range(self.mans):
lines[i][h] = newLines[i][h]
fit[i] = nextFit[i]
t += 1
# 上代中最优的为适应函数最小的
m = fit[0]
ml = 0
for i in range(self.rows):
if m < fit[i]:
m = fit[i]
ml = i
print "迭代完成"
# 输出结果:
calFitness.fitness(self.dep_num, self.dis_cus_cus, self.dis_sta_cus,
self.dis_dep_cus, self.min_dis_station,
self.time_win, self.tolerate_time_win, self.demand,
lines[ml], True)
print "最优权值为: %f" % (m)
print "最优结果为:"
for i in range(self.mans):
print "%d," % (lines[ml][i]),
print " "
print " "
print " "