遗传算法解决TSP问题（三种交叉）
代码参考博客：https://blog.csdn.net/springtostring/article/details/82429604 改进
算法参考：https://blog.csdn.net/u012750702/article/details/54563515
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
import math
import random
matplotlib.rcParams['font.family'] = 'STSong'
inputfile = ['eil51.txt','eil76.txt','eil101.txt',
'str70.txt','kroA100.txt','kroC100.txt',
'kroD100.txt', 'lin105.txt','pcb442.txt','pr2392.txt']
outputfile = ['eil51.outcome.txt','eil76.outcome.txt','eil101.outcome.txt',
'str70.outcome.txt','kroA100.outcome.txt','kroC100.outcome.txt',
'kroD100.outcome.txt', 'lin105.outcome.txt','pcb442.outcome.txt','pr2392.outcome.txt']
# 改良次数
improve_count = 2000
# 进化次数
evolution_time=[5000,10000,20000]
# 种群数
counts = [10,20,50,2000]
# 设置强者的定义概率，即种群前30%为强者
retain_rate = 0.3
# 设置弱者的存活概率
random_select_rate = 0.5
# 变异率
mutation_rate = 0.05
class TspHandler:
def __init__(self,infile,outfile,count):
# 种群数
self.count = count
self.city_name = []
self.city_condition = []
self.Distance = []
self.register=[]
self.read_file(infile)
i=0
population = self.population
while i < 20001:
# 选择繁殖个体群
parents = self.selection(population)
# 交叉繁殖
children = self.crossover1(parents)
# 变异操作
self.mutation(children)
# 更新种群
population = parents + children
distance, result_path = self.get_result(population)
self.register.append(distance)
i+=1
if ( i==20000):
X = []
Y = []
for index in result_path:
X.append(self.city_condition[index-1][0] )
Y.append(self.city_condition[index-1][1])
plt.plot(X, Y, '-o')
plt.show()
plt.plot(list(range(len(self.register))), self.register)
plt.show()
result_path = [self.origin] + result_path + [self.origin]
# with open('./outcomes/' + outfile+'1', 'a', encoding='UTF-8') as f:
# print(count, i)
# print(distance)
# print(result_path)
# f.write('population:' + str(count) + ' ' + 'generations：' + str(i) + '\n')
# f.write('全程路程：' + str(distance) + '\n')
# f.write('路线：')
# f.write(str(result_path) + '\n')
# 总距离
def get_total_distance(self,x):
distance = 0
distance += self.Distance[self.origin-1][x[0]-1]
d =len(x)
for i in range(d):
if i == d - 1:
distance += self.Distance[self.origin-1][x[i]-1]
else:
distance += self.Distance[x[i]-1][x[i + 1]-1]
return distance
# 改良
def improve(self,x):
i = 0
distance = self.get_total_distance(x)
d = len(x)
while i < improve_count:
# randint [a,b]
u = random.randint(0, d - 1)
v = random.randint(0, d - 1)
if u != v:
new_x = x.copy()
t = new_x[u]
new_x[u] = new_x[v]
new_x[v] = t
new_distance = self.get_total_distance(new_x)
if new_distance < distance:
distance = new_distance
x = new_x.copy()
else:
continue
i += 1
# 自然选择
def selection(self,population):
"""
选择
先对适应度从大到小排序，选出存活的染色体
再进行随机选择，选出适应度虽然小，但是幸存下来的个体
"""
# 对总距离从小到大进行排序
graded = [[self.get_total_distance(x), x] for x in population]
graded = [x[1] for x in sorted(graded)]
# 选出适应性强的染色体
retain_length = int(len(graded) * retain_rate)
parents = graded[:retain_length]
# 选出适应性不强，但是幸存的染色体
for chromosome in graded[retain_length:]:
if random.random() < random_select_rate:
parents.append(chromosome)
return parents
def crossover1(self,parents):
d = len(parents)
target_count = self.count - d
children = []
while len(children) < target_count:
male_index = random.randint(0, d - 1)
female_index = random.randint(0, d - 1)
if male_index != female_index:
male = parents[male_index]
female = parents[female_index]
d1 = len(male)
star = random.randint(0, d1- 1)
if female[star] != male[star]:
index = 1
else:
index = 0
gene1 = []
gene2 = []
gene1.append(male[star])
gene2.append(female[star])
while (index == 1):
for i in range(d1):
if len(gene2) == d1:
index = 0
if male[i] == female[star]:
star = i
gene1.append(male[star])
gene2.append(female[star])
if (gene1[0] == female[i]):
index = 0
break
exchange_shine = []
for i in range(len(gene1)):
exchange_shine.append([gene1[i], gene2[i]])
children1 = male.copy()
children2 = female.copy()
d2 = len(exchange_shine)
for i in range(d1):
for j in range(d2):
if (children1[i] == exchange_shine[j][0]):
children1[i] = exchange_shine[j][1]
break
for m in range(d2):
if (children2[i] == exchange_shine[m][1]):
children2[i] = exchange_shine[m][0]
break
children.append(children1)
if (len(children) < target_count):
children.append(children2)
return children
def crossover2(self,parents):
d = len(parents)
target_count = self.count - d
children = []
while len(children) < target_count:
male_index = random.randint(0, d - 1)
female_index = random.randint(0, d - 1)
if male_index != female_index:
male = parents[male_index]
female = parents[female_index]
left = random.randint(0, len(male) - 2)
right = random.randint(left + 1, len(male) - 1)
gene1 = male[left:right]
gene2 = []
children1 = male.copy()
children2 = female.copy()
index = 0
d1 = len(female)
for i in range(d1):
if female[i] in gene1:
gene2.append(female[i])
children2[i] = gene1[index]
index += 1
children1[left:right] = gene2
children.append(children1)
children.append(children2)
return children
# 交叉繁殖
def crossover3(self,parents):
# 生成子代的个数,以此保证种群稳定
target_count = self.count - len(parents)
# 孩子列表
children = []
while len(children) < target_count:
male_index = random.randint(0, len(parents) - 1)
female_index = random.randint(0, len(parents) - 1)
if male_index != female_index:
male = parents[male_index]
female = parents[female_index]
left = random.randint(0, len(male) - 2)
right = random.randint(left + 1, len(male) - 1)
# 交叉片段
gene1 = male[left:right]
gene2 = female[left:right]
child1_c = male[right:] + male[:right]
child2_c = female[right:] + female[:right]
child1 = child1_c.copy()
child2 = child2_c.copy()
for o in gene2:
child1_c.remove(o)
for o in gene1:
child2_c.remove(o)
child1[left:right] = gene2
child2[left:right] = gene1
d =len(child1)
child1[right:] = child1_c[0:d - right]
child1[:left] = child1_c[d - right:]
child2[right:] = child2_c[0:d - right]
child2[:left] = child2_c[d - right:]
children.append(child1)
children.append(child2)
return children
# 变异
def mutation(slef,children):
for i in range(len(children)):
if random.random() < mutation_rate:
child = children[i]
d =len(child)
u = random.randint(1, d - 4)
v = random.randint(u + 1, d - 3)
w = random.randint(v + 1, d - 2)
child = children[i]
child = child[0:u] + child[v:w] + child[u:v] + child[w:]
children[i] = child
return children
# 得到最佳纯输出结果
def get_result(self,population):
graded = [[self.get_total_distance(x), x] for x in population]
graded = sorted(graded)
return graded[0][0], graded[0][1]
def read_file(self,file_path):
with open(file_path, 'r', encoding='UTF-8') as f:
city_condition = []
lines = f.readlines()
for line in lines:
line = line.split('\n')[0]
line = line.split(' ')
self.city_name.append(line[0])
city_condition.append([float(line[1]), float(line[2])])
self.city_condition = np.array(city_condition)
# 展示地图
# plt.scatter(city_condition[:,0],city_condition[:,1])
# plt.show()
# 距离矩阵
city_count = len(self.city_name)
self.Distance = np.zeros([city_count, city_count])
for i in range(city_count):
for j in range(city_count):
self.Distance[i][j] = math.sqrt(
(city_condition[i][0] - city_condition[j][0]) ** 2 + (
city_condition[i][1] - city_condition[j][1]) ** 2)
self.population = []
# 设置起点
self.origin = 15
index = [i for i in range(city_count)]
index.remove(15)
# 使用改良圈算法初始化种群
for i in range(self.count):
# 随机生成个体
x = index.copy()
random.shuffle(x)
self.improve(x)
self.population.append(x)
if __name__ == '__main__':
for infile,outfile in zip(inputfile,outputfile):
for count in counts:
a = TspHandler(infile,outfile,count)