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import numpy as np
import random
import operator
import pandas as pd
import matplotlib.pyplot as plt
from operator import itemgetter
class City:
def __init__(self, x, y):
self.x = x
self.y = y
def distance(self, city):
x_dis = abs(self.x - city.x)
y_dis = abs(self.y - city.y)
distance = np.sqrt((x_dis ** 2) + (y_dis ** 2))
return distance
def __repr__(self):
return "(" + str(self.x) + "," + str(self.y) + ")"
# 越小越好
class Fitness:
def __init__(self, route):
self.route = route
self.distance = 0
self.fitness = 0.0
def route_distance(self):
if self.distance == 0:
path_distance = 0
for i in range(0, len(self.route)):
from_city = self.route[i]
to_city = None
if i + 1 < len(self.route):
to_city = self.route[i + 1]
else:
to_city = self.route[0]
path_distance += from_city.distance(to_city)
self.distance = path_distance
return self.distance
def route_fitness(self):
if self.fitness == 0:
self.fitness = 1 / float(self.route_distance())
return self.fitness
def createRoute(cityList):
route = random.sample(cityList, len(cityList))
return route
def initialPopulation(popSize, cityList):
population = []
for i in range(0, popSize):
population.append(createRoute(cityList))
return population
def rankRoutes(population):
fitnessResults = {}
for i in range(0,len(population)):
fitnessResults[i] = Fitness(population[i]).route_fitness()
return sorted(fitnessResults.items(), key=operator.itemgetter(1), reverse=True)
def selection(popRanked, eliteSize):
selectionResults = []
df = pd.DataFrame(np.array(popRanked), columns=["Index", "Fitness"])
df['cum_sum'] = df.Fitness.cumsum()
df['cum_perc'] = 100 * df.cum_sum / df.Fitness.sum()
for i in range(0, eliteSize):
selectionResults.append(popRanked[i][0])
for i in range(0, len(popRanked) - eliteSize):
pick = 100 * random.random()
for i in range(0, len(popRanked)):
if pick <= df.iat[i, 3]:
selectionResults.append(popRanked[i][0])
break
return selectionResults
def matingPool(population, selectionResults):
matingpool = []
for i in range(0, len(selectionResults)):
index = selectionResults[i]
matingpool.append(population[index])
return matingpool
def breed(parent1, parent2):
child = []
childP1 = []
childP2 = []
geneA = int(random.random() * len(parent1))
geneB = int(random.random() * len(parent1))
startGene = min(geneA, geneB)
endGene = max(geneA, geneB)
# save_sub_city_list = parent1[startGene: endGene]
# target_index = 0
# for i in range(len(parent2)):
# while startGene < len(child) < endGene-1:
# child.append(save_sub_city_list[target_index])
# target_index += 1
# continue
# if parent2[i] not in save_sub_city_list:
# child.append(parent2[i])
for i in range(startGene, endGene):
childP1.append(parent1[i])
childP2 = [item for item in parent2 if item not in childP1]
child = childP1 + childP2
return child
def breedPopulation(matingpool, eliteSize):
children = []
length = len(matingpool) - eliteSize
pool = random.sample(matingpool, len(matingpool))
for i in range(0, eliteSize):
children.append(matingpool[i])
for i in range(0, length):
child = breed(pool[i], pool[len(matingpool) - i - 1])
children.append(child)
return children
def mutate(individual, mutationRate):
for swapped in range(len(individual)):
if (random.random() < mutationRate):
swapWith = int(random.random() * len(individual))
city1 = individual[swapped]
city2 = individual[swapWith]
individual[swapped] = city2
individual[swapWith] = city1
return individual
def mutatePopulation(population, mutationRate):
mutatedPop = []
for ind in range(0, len(population)):
mutatedInd = mutate(population[ind], mutationRate)
mutatedPop.append(mutatedInd)
return mutatedPop
def nextGeneration(currentGen, eliteSize, mutationRate):
popRanked = rankRoutes(currentGen)
selectionResults = selection(popRanked, eliteSize)
matingpool = matingPool(currentGen, selectionResults)
children = breedPopulation(matingpool, eliteSize)
nextGeneration = mutatePopulation(children, mutationRate)
return nextGeneration
def geneticAlgorithm(population, popSize, eliteSize, mutationRate, generations):
pop = initialPopulation(popSize, population)
print("Initial distance: " + str(1 / rankRoutes(pop)[0][1]))
for i in range(0, generations):
pop = nextGeneration(pop, eliteSize, mutationRate)
print(f"generation: {i}, distance: {1 / rankRoutes(pop)[0][1]}")
print("Final distance: " + str(1 / rankRoutes(pop)[0][1]))
bestRouteIndex = rankRoutes(pop)[0][0]
bestRoute = pop[bestRouteIndex]
return bestRoute
def geneticAlgorithmPlot(population, popSize, eliteSize, mutationRate, generations):
pop = initialPopulation(popSize, population)
progress = []
progress.append(1 / rankRoutes(pop)[0][1])
for i in range(0, generations):
pop = nextGeneration(pop, eliteSize, mutationRate)
progress.append(1 / rankRoutes(pop)[0][1])
plt.plot(progress)
plt.ylabel('Distance')
plt.xlabel('Generation')
plt.show()
if __name__ == '__main__':
cityList = []
for i in range(0, 25):
cityList.append(City(x=int(random.random() * 200), y=int(random.random() * 200)))
# plt.scatter(list(map(lambda city: city.x, cityList)), list(map(lambda city: city.y, cityList)))
# plt.show()
best_route = geneticAlgorithm(population=cityList, popSize=100, eliteSize=20, mutationRate=0.01, generations=400)
# geneticAlgorithmPlot(population=cityList, popSize=100, eliteSize=20, mutationRate=0.01, generations=500)
print()
# for i in range(0, len(best_route), 2):
x = list(map(lambda city: city.x, best_route))
y = list(map(lambda city: city.y, best_route))
plt.scatter(x, y)
plt.plot(x, y, 'ro-')
plt.show()
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