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| import os
import random
import math
import time
import matplotlib.pyplot as plt
class GeneticAlgTSP():
def initial_population(self, amount, length, elite):
population = []
if elite != []:
elite = self.mutate(elite)
population.append(elite)
while len(population) != amount:
individual = random.sample(range(length), length)
if individual not in population:
population.append(individual)
return population
def initial_cities(self, filename):
cities = []
diskpath = os.path.dirname(__file__)
filepath = diskpath + '//TSP数据库//'+ filename
with open(filepath, 'r') as file:
lines = file.readlines()
for line in lines:
if line.startswith("EOF"):
break
data = line.split()
if len(data) == 3 and data[0].isdigit():
x = float(data[1])
y = float(data[2])
cities.append((x, y))
return cities
def __init__(self, filename, elite):
self.cities = self.initial_cities(filename)
self.population = self.initial_population(10, len(self.cities), elite)
def random2point(self, size):
point1 = random.randint(1, size - 1)
point2 = random.randint(1, size - 1)
while point1 == point2:
point2 = random.randint(1, size - 1)
return min(point1, point2), max(point1, point2)
def euclidean_distance(self, city1, city2):
x1, y1 = city1
x2, y2 = city2
distance = math.sqrt((x2 - x1)**2 + (y2 - y1)**2)
return distance
def fitness(self, individual):
amount = len(individual)
distance = 0
for i in range(amount):
city1 = self.cities[individual[i]]
city2 = self.cities[individual[(i + 1) % amount]]
distance += self.euclidean_distance(city1, city2)
return int(distance)
def tournament_select(self, population):
group1 = random.sample(population, 8)
parent1 = min(group1, key=self.fitness)
group2 = random.sample(population, 8)
if parent1 in group2:
group2.remove(parent1)
parent2 = min(group2, key=self.fitness)
return parent1, parent2
def crossover(self, parent1, parent2):
size = len(parent1)
child1, child2 = [None] * size, [None] * size
point1, point2 = self.random2point(size)
child1[point1:point2 + 1] = parent2[point1:point2 + 1]
child2[point1:point2 + 1] = parent1[point1:point2 + 1]
mapping1 = {parent2[i]: parent1[i] for i in range(point1, point2 + 1)}
mapping2 = {parent1[i]: parent2[i] for i in range(point1, point2 + 1)}
for i in range(size):
if i < point1 or i > point2:
element1 = parent1[i]
while element1 in mapping1:
element1 = mapping1[element1]
child1[i] = element1
element2 = parent2[i]
while element2 in mapping2:
element2 = mapping2[element2]
child2[i] = element2
return child1, child2
def mutate(self, individual):
size = len(individual)
point1, point2 = self.random2point(size)
mutate_individual = individual[:point1] + individual[point1:point2 + 1][::-1] + individual[point2 + 1:]
return mutate_individual
def GA(self, max_iterations, max_populations, mutate_rate):
iteration = 0
while iteration != max_iterations:
size = len(self.population)
parent1, parent2 = self.tournament_select(self.population)
child1, child2 = self.crossover(parent1, parent2)
self.population = sorted(self.population, key=self.fitness)
if size == max_populations:
self.population[size - 1], self.population[size - 2] = child1, child2
elif size <= 18:
self.population.append(child1)
self.population.append(child2)
self.population = sorted(self.population, key=self.fitness)
rate = mutate_rate
for i in range(len(self.population)):
if random.random() < mutate_rate:
self.population[i] = self.mutate(self.population[i])
rate += 0.01
iteration += 1
self.population = sorted(self.population, key=self.fitness)
return self.fitness(self.population[0]), self.population[0]
def plot_path(points, path):
plt.cla()
x_values = [point[0] for point in points]
y_values = [point[1] for point in points]
plt.scatter(x_values, y_values, color='blue', s=15)
for i in range(len(path) - 1):
point1 = points[path[i]]
point2 = points[path[i + 1]]
plt.plot([point1[0], point2[0]], [point1[1], point2[1]], color='red', linewidth=0.1)
point1 = points[path[-1]]
point2 = points[path[0]]
plt.plot([point1[0], point2[0]], [point1[1], point2[1]], color='red', linewidth=0.1)
plt.title('Visualization of Path')
plt.xlabel('X')
plt.ylabel('Y')
plt.legend(['points', 'path'], loc='upper right')
plt.draw()
plt.pause(0.2)
def EA(filename):
max_iterations = 10000
max_generations = 100
max_populations = 20
max_blocktime = 15
mutate_rate = 0.08
generation = 0
best_distance = float('inf')
best_path = []
blocktime = 0
while generation != max_generations:
solve = GeneticAlgTSP(filename, best_path)
distance, path = solve.GA(max_iterations, max_populations, mutate_rate)
if distance < best_distance or blocktime == max_blocktime:
if distance < best_distance:
best_distance = distance
best_path = path
print("第", generation + 1, "次路径长度:", best_distance)
plot_path(solve.cities, best_path)
blocktime = 0
generation += 1
continue
blocktime += 1
return best_distance, best_path
filename = '//' + 'rw1621'+ '.tsp'
best_distance,best_path = EA(filename)
print("最佳路线:",best_path)
print("最短距离",best_distance)
|
