sigmod = exp(-(newl - oldl)/T) if tmp < sigmod: x = newx xx.append(x) # xx[n,:] = x di.append(newl) # di[n]= newl k += 1 m += 1 # 内循环次数加1 # 内循环 iteration += 1 # 外循环次数加1 T = T*Lambda # 降温 # 计算最优值 bestd = min(di) indx = argmin(di) bestx = xx[indx] print "循环迭代",k,"次" print "最优解:",bestd print "最佳路线:",bestx # 优化前城市图,路径图 Untils.drawScatter(cityPosition,flag=False) Untils.drawPath(range(m-1),cityPosition) # 显示优化后城市图,路径图 Untils.drawScatter(cityPosition,flag=False) Untils.drawPath(bestx,cityPosition,color='r') # 绘制误差趋势线 x0 = range(len(di)); Untils.TrendLine(x0,di)
# -*- coding:utf-8 -*-
# Filename : testBoltzmann01.py
import operator
import copy
import Untils
import Boltzmann
from numpy import *
import matplotlib.pyplot as plt
dataSet = Untils.loadDataSet("dataSet25.txt")
cityPosition = mat(dataSet)
m, n = shape(cityPosition)
bestx, di = Boltzmann.boltzmann(cityPosition, MAX_ITER=1000, T0=100)
# 优化前城市图,路径图
Untils.drawScatter(cityPosition, flag=False)
Untils.drawPath(list(range(m)), cityPosition)
# 显示优化后城市图,路径图
Untils.drawScatter(cityPosition, flag=False)
Untils.drawPath(bestx, cityPosition, color='b')
# 绘制误差趋势线
x0 = list(range(len(di)))
Untils.TrendLine(x0, di)
sigmod = exp(-(newl - oldl) / T)
if tmp < sigmod:
x = newx
xx.append(x) # xx[n,:] = x
di.append(newl) # di[n]= newl
k += 1
m += 1 # 内循环次数加1
# 内循环
iteration += 1 # 外循环次数加1
T = T * Lambda # 降温
# 计算最优值
bestd = min(di)
indx = argmin(di)
bestx = xx[indx]
print "循环迭代", k, "次"
print "最优解:", bestd
print "最佳路线:", bestx
# 优化前城市图,路径图
Untils.drawScatter(cityPosition, flag=False)
Untils.drawPath(range(m - 1), cityPosition)
# 显示优化后城市图,路径图
Untils.drawScatter(cityPosition, flag=False)
Untils.drawPath(bestx, cityPosition, color='r')
# 绘制误差趋势线
x0 = range(len(di))
Untils.TrendLine(x0, di)