166 lines
5.4 KiB
Python
166 lines
5.4 KiB
Python
import numpy as np
|
|
import random
|
|
import matplotlib.pyplot as plt
|
|
import copy
|
|
|
|
# 各个城市的坐标
|
|
# City_Map = [[106.54,29.59]
|
|
# ,[91.11,29.97]
|
|
# ,[87.68,43.77]
|
|
# ,[106.27,38.47]
|
|
# ,[111.65,40.82]
|
|
# ,[108.33,22.84]
|
|
# ,[126.63,45.75]
|
|
# ,[125.35,43.88]
|
|
# ,[123.38,41.8]
|
|
# ,[114.48,38.03]
|
|
# ,[112.53,37.87]
|
|
# ,[101.74,36.56]
|
|
# ,[117,36.65]
|
|
# ,[113.6,34.76]
|
|
# ,[118.78,32.04]
|
|
# ,[117.27,31.86]]
|
|
|
|
City_Map = 100 * np.random.rand(20, 2) # 随机产生20个城市
|
|
|
|
DNA_SIZE = len(City_Map) # 编码长度
|
|
POP_SIZE = 200 # 种群大小
|
|
CROSS_RATE = 0.6 # 交叉率
|
|
MUTA_RATE = 0.2 # 变异率
|
|
Iterations = 1000 # 迭代次数
|
|
|
|
|
|
def distance(DNA): # 根据DNA的路线计算距离
|
|
dis = 0
|
|
temp = City_Map[DNA[0]]
|
|
for i in DNA[1:]:
|
|
dis = dis + ((City_Map[i][0] - temp[0]) ** 2 + (City_Map[i][1] - temp[1]) ** 2) ** 0.5
|
|
temp = City_Map[i]
|
|
return dis + ((temp[0] - City_Map[DNA[0]][0]) ** 2 + (temp[1] - City_Map[DNA[0]][1]) ** 2) ** 0.5
|
|
|
|
|
|
def getfitness(pop): # 计算种群适应度，这里适应度用距离的倒数表示
|
|
temp = []
|
|
for i in range(len(pop)):
|
|
temp.append(1 / (distance(pop[i])))
|
|
return temp - np.min(temp)
|
|
|
|
|
|
def select(pop, fitness): # 根据适应度选择，以赌轮盘的形式，适应度越大的个体被选中的概率越大
|
|
s = fitness.sum()
|
|
temp = np.random.choice(np.arange(len(pop)), size=POP_SIZE, replace=True, p=(fitness / s))
|
|
p = []
|
|
for i in temp:
|
|
p.append(pop[i])
|
|
return p
|
|
|
|
|
|
def mutation(DNA, MUTA_RATE): # 进行变异
|
|
if np.random.rand() < MUTA_RATE: # 以MUTA_RATE的概率进行变异
|
|
mutate_point1 = np.random.randint(0, DNA_SIZE) # 随机产生一个实数，代表要变异基因的位置
|
|
mutate_point2 = np.random.randint(0, DNA_SIZE) # 随机产生一个实数，代表要变异基因的位置
|
|
while (mutate_point1 == mutate_point2): # 保证2个所选位置不相等
|
|
mutate_point2 = np.random.randint(0, DNA_SIZE)
|
|
DNA[mutate_point1], DNA[mutate_point2] = DNA[mutate_point2], DNA[mutate_point1] # 2个所选位置进行互换
|
|
|
|
|
|
def crossmuta(pop, CROSS_RATE): # 交叉变异
|
|
new_pop = []
|
|
for i in range(len(pop)): # 遍历种群中的每一个个体，将该个体作为父代
|
|
n = np.random.rand()
|
|
if n >= CROSS_RATE: # 大于交叉概率时不发生变异，该子代直接进入下一代
|
|
temp = pop[i].copy()
|
|
new_pop.append(temp)
|
|
|
|
if n < CROSS_RATE: # 小于交叉概率时发生变异
|
|
list1 = pop[i].copy()
|
|
list2 = pop[np.random.randint(POP_SIZE)].copy() # 选取种群中另一个个体进行交叉
|
|
status = True
|
|
while status: # 产生2个不相等的节点，中间部分作为交叉段，采用部分匹配交叉
|
|
k1 = random.randint(0, len(list1) - 1)
|
|
k2 = random.randint(0, len(list2) - 1)
|
|
if k1 < k2:
|
|
status = False
|
|
|
|
k11 = k1
|
|
|
|
fragment1 = list1[k1: k2]
|
|
fragment2 = list2[k1: k2]
|
|
|
|
list1[k1: k2] = fragment2
|
|
list2[k1: k2] = fragment1
|
|
|
|
del list1[k1: k2]
|
|
left1 = list1
|
|
|
|
offspring1 = []
|
|
for pos in left1:
|
|
if pos in fragment2:
|
|
pos = fragment1[fragment2.index(pos)]
|
|
while pos in fragment2:
|
|
pos = fragment1[fragment2.index(pos)]
|
|
offspring1.append(pos)
|
|
continue
|
|
offspring1.append(pos)
|
|
for i in range(0, len(fragment2)):
|
|
offspring1.insert(k11, fragment2[i])
|
|
k11 += 1
|
|
temp = offspring1.copy()
|
|
mutation(temp, MUTA_RATE)
|
|
|
|
new_pop.append(temp) # 把部分匹配交叉后形成的合法个体加入到下一代种群
|
|
|
|
return new_pop
|
|
|
|
|
|
def print_info(pop): # 用于输出结果
|
|
fitness = getfitness(pop)
|
|
maxfitness = np.argmax(fitness) # 得到种群中最大适应度个体的索引
|
|
# 打印结果
|
|
print("最优的基因型：", pop[maxfitness])
|
|
print("最短距离：", distance(pop[maxfitness]))
|
|
# 按最优结果顺序把地图上的点加入到best_map列表中
|
|
best_map = []
|
|
for i in pop[maxfitness]:
|
|
best_map.append(City_Map[i])
|
|
best_map.append(City_Map[pop[maxfitness][0]])
|
|
X = np.array((best_map))[:, 0]
|
|
Y = np.array((best_map))[:, 1]
|
|
# 绘制地图以及路线
|
|
plt.figure()
|
|
plt.rcParams['font.sans-serif'] = ['SimHei']
|
|
plt.scatter(X, Y)
|
|
for dot in range(len(X) - 1):
|
|
plt.annotate(pop[maxfitness][dot], xy=(X[dot], Y[dot]), xytext=(X[dot], Y[dot]))
|
|
plt.annotate('start', xy=(X[0], Y[0]), xytext=(X[0] + 1, Y[0]))
|
|
plt.plot(X, Y)
|
|
|
|
|
|
if __name__ == "__main__": # 主循环
|
|
# 生成初代种群pop
|
|
pop = []
|
|
list = list(range(DNA_SIZE))
|
|
for i in range(POP_SIZE):
|
|
random.shuffle(list)
|
|
l = list.copy()
|
|
pop.append(l)
|
|
best_dis = []
|
|
# 进行选择，交叉，变异，并把每代的最优个体保存在best_dis中
|
|
for i in range(Iterations): # 迭代N代
|
|
pop = crossmuta(pop, CROSS_RATE)
|
|
fitness = getfitness(pop)
|
|
maxfitness = np.argmax(fitness)
|
|
best_dis.append(distance(pop[maxfitness]))
|
|
pop = select(pop, fitness) # 选择生成新的种群
|
|
|
|
print_info(pop) # 打印信息
|
|
|
|
print('逐代的最小距离：', best_dis)
|
|
|
|
# 画图
|
|
plt.figure()
|
|
plt.plot(range(Iterations), best_dis)
|
|
plt.show()
|
|
plt.close()
|
|
|