c语言推销员旅行问题遗传算法,基于遗传算法的旅行推销员
我试图用python和遗传算法来可视化和解决旅行销售人员的问题，我在youtube上关注Daniel Shiffman的一个编码挑战视频我不知道是否允许我在这里放一个链接，但这里是链接(https://www.youtube.com/watch?v=M3KTWnTrU_c&list=PLRqwX-V7Uu6ZiZxtDDRCi6uhfTH4FilpH&index=42)
所以问题是，我的遗传算法没有进化。它仅仅停留在第一代上。我想我无法写出创建下一代的代码，因为我缺乏python知识，下面是代码：import pygame
import random
import numpy as np
import sys
size=width,height=500,500
bgColor = (0,0,0)
screen = pygame.display.set_mode(size)
vertexArray = []
fitness = []
population = []
pygame.display.set_caption("Finding the Shortest path with Genetich Algorithm")
bestEver=[]
bestDistance = 999999
def shuffle(vertexArray):
np.random.shuffle(vertexArray)
def swap(a,i,j):
temp = a[i]
a[i]=a[j]
a[j]=temp
class Vertex:
def __init__(self,x,y):
self.color = (255,255,255)
self.x = x
self.y = y
def display(self):
pygame.draw.circle(screen,self.color,(self.x,self.y),4,4)
def getPosition(self):
return [self.x,self.y]
def __str__(self):
return str(self.x+", "+self.y)
def createVertexes(numOfVertexes):
for i in range(numOfVertexes):
vertexArray.append(Vertex(random.randint(0,width),random.randint(0,height)))
createVertexes(8)
def createPopulation():
for i in range(300):
population.append(vertexArray[:])
for j in range(100):
shuffle(population[i])
createPopulation()
def drawLines(vertexArray,color):
for i in range(vertexArray.__len__()-1):
pygame.draw.line(screen,color,vertexArray[i].getPosition(),vertexArray[i+1].getPosition(),1)
def calculateDistance(vertexArray):
dist = 0
for i in range(vertexArray.__len__()-1):
dist += np.linalg.norm(np.asarray(vertexArray[i+1].getPosition())-np.asarray(vertexArray[i].getPosition()))
return dist
def displayVertexes(vertexArray):
for i in range(vertexArray.__len__()):
vertexArray[i].display()
def calculateFitness():
global bestDistance
global bestEver
for i in range(population.__len__()-1):
d = calculateDistance(population[i])
if d
bestDistance = d
bestEver = population[i][:]
fitness.append(1/d)
def getProbability(fitness,population):
first = 0
sum = 0
for i in range(fitness.__len__() - 1):
sum += fitness[i]
r = random.uniform(0, sum)
for i in range(fitness.__len__() - 1):
if r<=fitness[i] and r>=first:
return population[i]
first = fitness[i]
return population[random.randint(0,2)]
def pickOne(population,fitness):
return getProbability(fitness,population)
def mutate(order):
indexA = random.randint(0,order.__len__()-1)
indexB = random.randint(0,order.__len__()-1)
swap(order,indexA,indexB)
def nextGeneration():
global population
newPopulation=[]
for i in range(population.__len__()-1):
order = pickOne(population,fitness)
mutate(order)
newPopulation.append(order)
population = newPopulation
while True:
for event in pygame.event.get():
if event.type == pygame.QUIT:
sys.exit()
screen.fill(bgColor)
displayVertexes(vertexArray)
calculateFitness()
print(fitness[2])
nextGeneration()
drawLines(bestEver,(255, 0, 0))
pygame.display.flip()