TSP with 退火

TSP/src/TSP.py

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+import numpy as np
+import matplotlib.pyplot as plt
+import pdb
+import random as rd
+from math import sqrt, exp
+from os import path
+from matplotlib.lines import Line2D
+import matplotlib.animation as animation
+"旅行商问题 ( TSP , Traveling Salesman Problem )"
+'''
+编写模拟退火算法解旅行商问题
+实现TSP的模拟退火算法函数，输入是矩阵表示的图，输出是路径
+可视化显示中间变化过程
+要有详细的注释
+使用python语言
+'''
+
+# input
+
+
+def loadin_data():
+    local_path = path.dirname(__file__)
+    with open(local_path + '/../testdata/1.txt', 'r') as f:
+        data = f.read()
+        data = data.split()
+        length = len(data)
+        x = []
+        y = []
+        for i in range(length//2):
+            x.append(int(data[2*i]))
+            y.append(int(data[2*i+1]))
+        f.close()
+        # print(data)
+        return x, y
+
+
+loadin_data()
+x, y = loadin_data()
+#print(x, y)
+path = [x, y]
+
+
+def distance(path, i, j):
+    a = path[0][i]-path[0][j]
+    b = path[1][i]-path[1][j]
+    return sqrt(a**2+b**2)
+
+
+# distance_matrix = []
+# for i in range(len(x)):
+#     distance_matrix.append([])
+# # print(distance_matrix)
+# for i in range(2):
+#     for j in range(len(x)):
+#         distance_matrix[i].append(distance(path, i, j))
+
+# print(distance_matrix[1])
+# print(len(distance_matrix[0]))
+
+
+def exchange(path, a, b):
+    path[0][a], path[0][b] = path[0][b], path[0][a]
+    path[1][a], path[1][b] = path[1][b], path[1][a]
+
+
+def sum_distance(path):
+    sum = 0
+    for i in range(len(path[0])-1):
+        sum += distance(path, i, i + 1)
+        # print(path)
+    sum += distance(path, len(path[0]) - 1, 0)
+    return sum
+
+
+
+
+ax1 = plt.axes([0,0,1,1])
+# ax2 = plt.axes([0.55,0.05,0.95,0.9])
+fig, ax = plt.subplots()
+def draw_line(path):
+    for j in range(len(path[0])-1):
+        line1 = [(path[0][j], path[1][j]), (path[0][j+1], path[1][j+1])]
+        (line1_xs, line1_ys) = zip(*line1)
+        ax.add_line(Line2D(line1_xs, line1_ys, linewidth=1, color='blue'))
+    line1 = [(path[0][0], path[1][0]),
+             (path[0][len(path[0])-1], path[1][len(path[0])-1])]
+    (line1_xs, line1_ys) = zip(*line1)
+    ax.add_line(Line2D(line1_xs, line1_ys,linewidth=1, color='blue'))
+
+
+T = 100
+alpha = 0.95
+iters = 1000
+current = sum_distance(path)
+print(current, '\n')
+next = 0
+count = 0
+xl=[]
+yl=[]
+plt.cla()
+plt.ion()
+while(True):
+    if(T < 0.05):
+        break
+    oldcurrent = current
+    for i in range(iters):
+        a, b = rd.sample(range(0, len(path[0])), 2)
+        newpath=[[],[]]
+        for x,y in zip(path[0],path[1]):
+            newpath[0].append(x)
+            newpath[1].append(y)
+        # print(newpath)
+        exchange(newpath, a, b)
+        # print(newpath)
+        next = sum_distance(newpath)
+        # print(path)
+        # print(newpath)
+        # print(next - current)
+        if(next < current):
+            current = next
+            path = newpath.copy()
+        elif (rd.random() < exp(-(next - current)/T)):
+            current = next
+            path = newpath.copy()
+    plt.cla()
+    draw_line(path)
+    ax.plot(path[0], path[1],'ro')
+    plt.pause(0.05) 
+    if(oldcurrent!=current): 
+        # print(count)
+        count += 1
+        xl.append(count)
+        yl.append(current)
+        ax1.plot(xl,yl)
+        oldcurrent=current
+
+    T = alpha * T
+    print(T)
+for x,y in zip(path[0],path[1]):
+    print("(",x,y,")-->",end='')
+print(current)
+plt.ioff()
+plt.show()

TSP/testdata/1.txt

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+41 49 
+35 17 
+55 45 
+55 20 
+15 30 
+25 30 
+20 50 
+10 43 
+55 60 
+30 60 
+20 65 
+50 35 
+30 25 
+15 10 
+30 5 
+10 20 
+5 30 
+20 40 
+15 60 
+45 65 
+45 20 
+45 10 
+55 5 
+65 35 
+65 20 
+45 30 
+35 40 
+41 37 
+64 42