我根据书上的代码去写的，最后运行的时候一直给我报IndexError: list index out of range，另外完全用书上的数据又没什么问题，可以正常出结果，随便改了个数字也会报错，这种情况应该怎么解决呢
PyCharm Community Edition 2023.1
看看你的代码。IndexError: list index out of range 是你想访问的数据超出列表已有数据范围。比如列表中只有3个数，你访问第4个或者更多。
import random, math, sys
from builtins import range, str, int, len, list, enumerate, print, sum, isinstance, object
import matplotlib.pyplot as plt # 画图
from copy import deepcopy
from tqdm import * # 进度条
DEBUG = False
geneNum = 100 # 种群数量
generationNum = 500 # 迭代次数
CENTER = 0 # 配送中心
HUGE = 9999999
VARY = 0.05 # 变异几率
n = 25 # 客户点数量
m = 2 # 加油站数量
k = 3 # 车辆数量
Q = 60 # 额定载重量, t
dis = 400 # 续航里程, km
costPerKilo = 7 # 油价
epu = 20 # 早到惩罚成本
lpu = 30 # 晚到惩罚成本
speed = 40 # 速度，km/h
# 坐标
X = [0, 10, 15, 24, 34, 42, 60, 62, 47, 46, 39, 67, 85, 80, 87, 95, 102, 104, 101, 119, 132, 140, 137, 129, 170,
160, 49, 115]
Y = [0, 17, 30, 53, 36, 16, 30, 41, 54, 66, 86, 73, 82, 52, 44, 17, 34, 49, 62, 82, 66, 54, 58, 22, 62, 37,40, 65]
# 需求量
t = [4.6, 2.9, 3.9, 8.1, 5.7, 2.5, 4.3, 5.1, 5.5, 3, 1.6, 5.9, 3.2, 6.8, 3.6, 5.6, 5.1, 6.1, 0.6, 8.8, 9.9, 1.7,
3.6, 5.2, 2.5, 0.1, 0.1]
# 最早到达时间
eh = [3, 3, 4, 2, 10, 5, 1, 4, 10, 15, 6, 4, 7, 10, 13, 16, 8, 15, 15, 4, 5, 4, 2, 3, 1, 0, 0]
# 最晚到达时间
lh = [4, 5, 7, 6, 11, 7, 9, 11, 12, 18, 9, 10, 9, 15, 14, 20, 11, 19, 16, 9, 10, 9, 11, 12, 9, 100, 100]
# 服务时间
h = [0.2, 0.3, 0.3, 0.3, 0.3, 0.5, 0.8, 0.4, 0.5, 0.7, 0.7, 0.6, 0.2, 0.2, 0.4, 0.1, 0.1, 0.2, 0.5, 0.2, 0.3, 0.2,
0.1, 0.4, 0.5, 0.4, 0.4]
class Gene:
def __init__(self, name='Gene', data=None):
self.name = name
self.length = n + m + 1
if data is None:
self.data = self._getGene(self.length)
else:
assert(self.length+k == len(data))
self.data = data
self.fit = self.getFit()
self.chooseProb = 0 # 选择概率
# 为产生初始群体 做预备
def _generate(self, length):
data = [i for i in range(1, length)]
random.shuffle(data)
data.insert(0, CENTER)
data.append(CENTER)
return data
# 为产生初始群体 做预备
def _insertZeros(self, data):
sum = 0
newData = []
for index, pos in enumerate(data):
sum += t[pos]
if sum > Q:
newData.append(CENTER)
sum = t[pos]
newData.append(pos)
return newData
"""产生初始群体___________________"""
def _getGene(self, length: object) -> object:
data = self._generate(length)
data = self._insertZeros(data)
return data
"""计算适应度___________________"""
def getFit(self):
fit = distCost = timeCost = overloadCost = fuelCost = 0
dist = [] # from this to next
# 计算距离
i = 1
while i < len(self.data):
calculateDist = lambda x1, y1, x2, y2: math.sqrt(((x1 - x2) ** 2) + ((y1 - y2) ** 2))
dist.append(calculateDist(X[self.data[i]], Y[self.data[i]], X[self.data[i - 1]], Y[self.data[i - 1]]))
i += 1
# 距离成本
distCost = sum(dist) * costPerKilo#距离和乘以油价
# 时间成本
timeSpent = 0
for i, pos in enumerate(self.data):
# 跳过第一个中心
if i == 0:
continue
# 新车
elif pos == CENTER:
timeSpent = 0
# 更新路上花费的时间
timeSpent += (dist[i - 1] / speed)
# 提前到达
if timeSpent < eh[pos]:
timeCost += ((eh[pos] - timeSpent) * epu)
timeSpent = eh[pos]
# 迟到
elif timeSpent > lh[pos]:
timeCost += ((timeSpent - lh[pos]) * lpu)
# 更新时间
timeSpent += h[pos]
# 过载成本和燃油成本
load = 0
distAfterCharge = 0
for i, pos in enumerate(self.data):
# 跳过第一个中心
if i == 0:
continue
# 在这里充电
if pos > n:
distAfterCharge = 0
# at center, re-load
elif pos == CENTER:
load = 0
distAfterCharge = 0
# normal
else:
load += t[pos]#需求量
distAfterCharge += dist[i - 1]
# 更新负载和燃料成本
overloadCost += (HUGE * (load > Q))
fuelCost += (HUGE * (distAfterCharge > dis))
fit = distCost + timeCost + overloadCost + fuelCost
return 1/fit
#计算每个个体的 选择概率 （每个个体概率=个体适应度值/总的适应度值
def updateChooseProb(self, sumFit):
self.chooseProb = self.fit / sumFit
#选择路径
def moveRandSubPathLeft(self):
path = random.randrange(k) # 选择路径索引，随机分成k段
index = self.data.index(CENTER, path+1) #移动到所选索引
# move first CENTER
locToInsert = 0
self.data.insert(locToInsert, self.data.pop(index))
index += 1
locToInsert += 1
# move data after CENTER
while self.data[index] != CENTER:
self.data.insert(locToInsert, self.data.pop(index))
index += 1
locToInsert += 1
assert(self.length+k == len(self.data))
# plot this gene in a new window
def plot(self):
Xorder = [X[i] for i in self.data]
Yorder = [Y[i] for i in self.data]
plt.plot(Xorder, Yorder, c='black', zorder=1)
plt.scatter(X, Y, zorder=2)
plt.scatter([X[0]], [Y[0]], marker='o', zorder=3)
plt.scatter(X[-m:], Y[-m:], marker='^', zorder=3)
plt.title(self.name)
plt.show()
# return a bunch of random genes
def getRandomGenes(size):
genes = []
for i in range(size):
assert isinstance(genes, object)
genes.append(Gene("Gene "+str(i)))
return genes
# 计算适应度和
def getSumFit(genes):
sumFit = 0
for gene in genes:
sumFit += gene.fit
return sumFit
# 更新选择概率
def updateChooseProb(genes):
sumFit = getSumFit(genes)
for gene in genes:
gene.updateChooseProb(sumFit)
# 计算累计概率
def getSumProb(genes):
sum = 0
for gene in genes:
sum += gene.chooseProb
return sum
# 选择复制，选择前 1/3
def choose(genes):
num = int(geneNum/6) * 2 # 选择偶数个，方便下一步交叉
# sort genes with respect to chooseProb
key = lambda gene: gene.chooseProb
genes.sort(reverse=True, key=key)
# return shuffled top 1/3
return genes[0:num]
# 交叉一对
def crossPair(gene1, gene2, crossedGenes):
gene1.moveRandSubPathLeft()
gene2.moveRandSubPathLeft()
newGene1 = []
newGene2 = []
# copy first paths
centers = 0
firstPos1 = 1
for pos in gene1.data:
firstPos1 += 1
centers += (pos == CENTER)
newGene1.append(pos)
if centers >= 2:
break
centers = 0
firstPos2 = 1
for pos in gene2.data:
firstPos2 += 1
centers += (pos == CENTER)
newGene2.append(pos)
if centers >= 2:
break
# copy data not exits in father gene
for pos in gene2.data:
if pos not in newGene1:
newGene1.append(pos)
for pos in gene1.data:
if pos not in newGene2:
newGene2.append(pos)
# add center at end
newGene1.append(CENTER)
newGene2.append(CENTER)
# 计算适应度最高的
key = lambda gene: gene.fit
possible = []
while gene1.data[firstPos1] != CENTER:
newGene = newGene1.copy()
newGene.insert(firstPos1, CENTER)
newGene = Gene(data=newGene.copy())
possible.append(newGene)
firstPos1 += 1
possible.sort(reverse=True, key=key)
assert(possible)
crossedGenes.append(possible[0])
key = lambda gene: gene.fit
possible = []
while gene2.data[firstPos2] != CENTER:
newGene = newGene2.copy()
newGene.insert(firstPos2, CENTER)
newGene = Gene(data=newGene.copy())
possible.append(newGene)
firstPos2 += 1
possible.sort(reverse=True, key=key)
crossedGenes.append(possible[0])
# 交叉
def cross(genes):
crossedGenes = []
for i in range(0, len(genes), 2):
crossPair(genes[i], genes[i+1], crossedGenes)
return crossedGenes
# 合并
def mergeGenes(genes, crossedGenes):
# sort genes with respect to chooseProb
key = lambda gene: gene.chooseProb
genes.sort(reverse=True, key=key)
pos = geneNum - 1
for gene in crossedGenes:
genes[pos] = gene
pos -= 1
return genes
# 变异一个
def varyOne(gene):
varyNum = 10
variedGenes = []
for i in range(varyNum):
p1, p2 = random.choices(list(range(1,len(gene.data)-2)), k=2)
newGene = gene.data.copy()
newGene[p1], newGene[p2] = newGene[p2], newGene[p1] # 交换
variedGenes.append(Gene(data=newGene.copy()))
key = lambda gene: gene.fit
variedGenes.sort(reverse=True, key=key)
return variedGenes[0]
# 变异
def vary(genes):
for index, gene in enumerate(genes):
# 精英主义，保留前三十
if index < 30:
continue
if random.random() < VARY:
genes[index] = varyOne(gene)
return genes
if __name__ == "__main__" and not DEBUG:
genes = getRandomGenes(geneNum) # 初始种群
# 迭代
for i in tqdm(range(generationNum)):
updateChooseProb(genes)
sumProb = getSumProb(genes)
chosenGenes = choose(deepcopy(genes)) # 选择
crossedGenes = cross(chosenGenes) # 交叉
genes = mergeGenes(genes, crossedGenes) # 复制交叉至子代种群
genes = vary(genes) # under construction
# sort genes with respect to chooseProb
key = lambda gene: gene.fit
genes.sort(reverse=True, key=key) # 以fit对种群排序
print('\r\n')
print('data:', genes[0].data)
print('fit:', genes[0].fit)
genes[0].plot() # 画出来
if DEBUG:
print("START")
gene = Gene()
print(gene.data)
gene.moveRandSubPathLeft()
print(gene.data)
print("FINISH")