python实现遗传算法（旅行商问题）
网上找到一篇不错的论文：
http://www.doc88.com/p-9078235973437.html
然后我用python对其做了算法实现，贴代码：
from matplotlib import pyplot as plt
import numpy as np
import random
def coordinate_init(size):
# 产生坐标字典
coordinate_dict = {}
coordinate_dict[0] = (-size, 0) # 起点是（-size，0）
up=1
for i in range(1, size + 1): # 顺序标号随机坐标
x=np.random.uniform(-size,size)
if np.random.randint(0,2)==up:
y=np.sqrt(size**2-x**2)
else:
y=-np.sqrt(size**2-x**2)
coordinate_dict[i]=(x,y)
coordinate_dict[size + 1] = (-size, 0) # 终点是（-size,0),构成一个圆
return coordinate_dict
def distance_matrix(coordinate_dict, size): # 生成距离矩阵
d = np.zeros((size + 2, size + 2))
for i in range(size + 1):
for j in range(size + 1):
if (i == j):
continue
if (d[i][j] != 0):
continue
x1 = coordinate_dict[i][0]
y1 = coordinate_dict[i][1]
x2 = coordinate_dict[j][0]
y2 = coordinate_dict[j][1]
distance = np.sqrt((x1 - x2) ** 2 + (y1 - y2) ** 2)
if (i == 0):
d[i][j] = d[size + 1][j] = d[j][i] = d[j][size + 1] = distance
else:
d[i][j] = d[j][i] = distance
return d
def path_length(d_matrix, path_list, size): # 计算路径长度
length = 0
for i in range(size + 1):
length += d_matrix[path_list[i]][path_list[i + 1]]
return length
def shuffle(my_list):#起点和终点不能打乱
temp_list=my_list[1:-1]
np.random.shuffle(temp_list)
shuffle_list=my_list[:1]+temp_list+my_list[-1:]
return shuffle_list
def product_len_probability(my_list,d_matrix,size,p_num):
len_list=[]
pro_list=[]
path_len_pro=[]
for path in my_list:
len_list.append(path_length(d,path,size))
max_len=max(len_list)+1e-10
gen_best_length=min(len_list)
gen_best_length_index=len_list.index(gen_best_length)
mask_list=np.ones(p_num)*max_len-np.array(len_list)
sum_len=np.sum(mask_list)
for i in range(p_num):
if(i==0):
pro_list.append(mask_list[i]/sum_len)
elif(i==p_num-1):
pro_list.append(1)
else:
pro_list.append(pro_list[i-1]+mask_list[i]/sum_len)
for i in range(p_num):
path_len_pro.append([my_list[i],len_list[i],pro_list[i]])
return my_list[gen_best_length_index],gen_best_length,path_len_pro
def choose_cross(population,p_num):
jump=np.random.random()
if jumppopulation[mid][2]:
low=mid
mid=int((low+high)/2)
elif jumpprduct_set:
break
if np.random.random()prduct_set:
break
if np.random.random() {0: (-50, 0), 1: (-48.49424982314113, -12.17816628605375), 2: (9.111061342180996, 49.162877877713946), 3: (-5.177802487038939, 49.73118097738293), 4: (-28.11906419040516, -41.34390195731257), 5: (-38.917605610534636, 31.391399674797604), 6: (47.49580752251565, 15.62524456717865), 7: (-6.1349940624823915, 49.622191082753545), 8: (-7.114661421590306, -49.49122743331524), 9: (-45.98755124771604, -19.62511478281569), 10: (-10.907208159626194, 48.79582779462383), 11: (11.535613615138459, 48.651100897330494), 12: (16.69100207851993, -47.131841143910805), 13: (49.92190405424485, 2.7934737512229932), 14: (21.635620902506773, 45.07660045037793), 15: (34.80965114207605, 35.89273167880879), 16: (-10.556283772662766, 48.8729462270387), 17: (-34.26986662576967, -36.40846387108302), 18: (29.108362245849747, -40.65345308045048), 19: (38.62445512198866, -31.751401016797416), 20: (0.6666921470255147, 49.99555501823232), 21: (33.397971470527224, -37.209884461710566), 22: (-12.979918591843784, 48.28583346437243), 23: (-5.747924357032666, 49.66851483169021), 24: (47.17922013912872, -16.556605541705437), 25: (26.763758657317197, -42.233887135011365), 26: (-42.271695978747516, 26.70400193005425), 27: (4.4298597129327035, 49.803376822498045), 28: (-23.768133125153255, 43.989497016276424), 29: (-36.36374544900421, -34.317313661183114), 30: (-42.41586057800746, 26.474417300991394), 31: (-5.10843255646467, 49.73835458492824), 32: (10.132932983122302, -48.96247204910667), 33: (-33.00921929564278, -37.55517862415468), 34: (33.06877220376812, 37.50275063159092), 35: (-6.947638052481068, 49.514950524985046), 36: (-6.315644697890875, -49.599522498205594), 37: (41.3780628679112, -28.06877113981281), 38: (-27.811302751680987, -41.55155158660554), 39: (-16.79218725418388, -47.095885671897335), 40: (-19.86513495419905, -45.88438092915125), 41: (3.641422053024421, -49.867224160080816), 42: (-11.270990243754596, -48.713086321082024), 43: (-5.685945775992117, -49.67564816519737), 44: (32.73452125193285, -37.79485571353272), 45: (-31.139436980627934, -39.119502355340636), 46: (-47.652625926583255, -15.140252385648182), 47: (-26.642952144042653, -42.31020091006722), 48: (44.9791425162565, 21.83750760737788), 49: (36.73862125617772, -33.9156852826996), 50: (-15.430519955614422, 47.55942655141445), 51: (-50, 0)}
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522.7587332118794
效果的话，只能算差强人意了。很明显最优路径是一个圈，没有交叉的，然而史上最优路径也不是一个圈，更不用说最后一代了。算法本身的缺陷吧，要做更多的调优和设计更好的算子，才能有一定概率得到最优解吧。
缺点如下：
1、早熟。这是最大的缺点，即算法对新空间的探索能力是有限的，也容易收敛到局部最优解。
2、大量计算。涉及到大量个体的计算，当问题复杂时，计算时间是个问题。
3、处理规模小。目前对于维数较高的问题，还是很难处理和优化的。
4、难于处理非线性约束。对非线性约束的处理，大部分算法都是添加惩罚因子，这是一笔不小的开支。
5、稳定性差。因为算法属于随机类算法，需要多次运算，结果的可靠性差，不能稳定的得到解。
最大优点：不需要知道怎么去解决一个问题，需要知道是，用什么方式对可行解编码，使得它能被遗传算法机制所利用。
整体来看，是不如模拟退火算法的，毕竟模拟退火在参数不错的条件下，得到全局最优解的概率收敛到1。而遗传算法只能说得到不错的解，至于是不是最优解，看运气了。。。以上。