import random
import math
import matplotlib.pyplot as plt
import city
class no: #该类表示每个点的坐标
def __init__(self,x,y):
self.x = x
self.y = y
def draw(t): #该函数用于描绘路线图
x = [0] * (m+1)
y = [0] * (m+1)
for i in range(m):
x[i] = p[t[i]].x
y[i] = p[t[i]].y
x[m] = p[t[0]].x
y[m] = p[t[0]].y
plt.plot(x,y,color='r',marker='*' )
plt.show()
def GA_TSP(pc_=0.9,pm_=0.2,n_=10,cross=1,epochs=10000):
'''与禁忌搜索算法的对比'''
global n,m,pc,pm,best,p,dna,value,way
p = []
pc = pc_
pm = pm_ #pc为交配概率 pm为变异概率
best = 1000 #best记录最优距离，初始化无限大
n = n_ #n:样本个数
m = 100 #m:城市个数
dna = [[0]*(m) for i in range(n)] #开辟n*m的数组
value = [0.0]*n #value数组记录个体适应度
way = [0]*m #way数组记录最优解路线
mycol() #数据输入
init() #群体初始化
a = []
for i in range(epochs): #控制进化次数
slove(cross)
a.append(best)
#draw(way) # 画图描绘路线
#print("The way is",way) # 打印路线，以序列表示
#print("************************")
return a
def mutation(x): # 变异操作函数
x1 = [0]*m
for i in range(m):
x1[i] = x[i]
a = random.randint(0,m-1) #随机选出两个点，将之间各点进行倒置
b = random.randint(0,m-1)
if(a > b):
a,b = b,a
le = b - a + 1
for i in range(le):
x[a+i] = x1[b-i]
def mycol(): #城市坐标输入
'''
p.append(no( 16 , 96 ))
p.append(no( 16 , 94 ))
p.append(no( 20 , 92 ))
p.append(no( 22 , 93 ))
p.append(no( 25 , 97 ))
p.append(no( 22 , 96 ))
p.append(no( 20 , 97 ))
p.append(no( 17 , 96 ))
p.append(no( 16 , 97 ))
p.append(no( 14 , 98 ))
p.append(no( 17 , 97 ))
p.append(no( 21 , 95 ))
p.append(no( 19 , 97 ))
p.append(no( 20 , 94 ))
'''
a = dict.values(city.china)
a = list(a)
for i in range(30):
p.append(no(a[i][0],a[i][1]))
def init(): #初始化函数 随机产生初始个体
vis = [0] * m
num = 0
for i in range(n):
for j in range(m):
vis[j] = 0
for j in range(m):
num = random.randint(0,m-1)
while(vis[num] == 1): # 第num个城市已被占用，需要重新选择num
num = random.randint(0,m-1)
vis[num] = 1 # 表示第num个城市被使用
dna[i][j] = num # 表示第j步去第num个城市
def get_value(t): #适应度计算，即计算当先线路的距离
# t就是dna[i],一个列表,长度为m
ans = 0.0
for i in range(1,m): #两点距离公式
ans += math.sqrt((p[t[i]].x-p[t[i-1]].x) * (p[t[i]].x-p[t[i-1]].x) + (p[t[i]].y-p[t[i-1]].y) * (p[t[i]].y-p[t[i-1]].y))
ans += math.sqrt((p[t[0]].x-p[t[m-1]].x) * (p[t[0]].x-p[t[m-1]].x) + (p[t[0]].y-p[t[m-1]].y) * (p[t[0]].y-p[t[m-1]].y))#计算首尾结点的距离
return ans
def find(x,num,a=0,b=99): #交叉算子运算时判断是否出现重复的城市id
for i in range(a,b+1):
if(num[i] == x):
return i
return -1
def cross2(x,y): # 均匀交叉法
x1 = [0] * m
y1 = [0] * m
sample = []
for i in range(m):
x1[i] = x[i]
y1[i] = y[i]
sample.append(random.randint(0,1))
#print(sample)
for i in range(m): #交叉运算
if(sample[i]==0): # 模板值为0则xy交换
if(y[i] not in x1):
x1[i] = y[i]
if(x[i] not in x1):
y1[i] = x[i]
def cross1(x,y): # 多点交叉法
x1 = [0]*m
y1 = [0]*m
for i in range(m):
x1[i] = x[i]
y1[i] = y[i]
a = random.randint(0,m-1) #随机产生两个点
b = random.randint(0,m-1)
if(a > b):
a,b = b,a
for i in range(a): #交叉运算
x1[i]=y[i]
y1[i]=x[i]
for i in range(b+1,m):
x1[i]=y[i]
y1[i]=x[i]
ob = 0
for i in range(m): #判断交叉的合法性并进行维护，直到交叉成功
if(i<a or i>b):
ob = find(x1[i],x1,a,b)
while(ob != -1):#x1[i]与x[ob]重复
x1[i] = y1[ob]#y1[ob]即y[ob],必与x[ob](x1[ob])不同,将之赋给x1[i]，出现不重复概率更高
ob = find(x1[i],x1,a,b)#再次检测
for i in range(m): #操作同上，维护另一新个体的交叉合法性。
if(i<a or i>b):
ob = find(y1[i],y1,a,b)
while(ob != -1):
y1[i] = x1[ob]
ob = find(y1[i],y1,a,b)
for i in range(m):
x[i] = x1[i]
y[i] = y1[i]
def cross(x,y): # 一点交叉法
x1 = [0]*m
y1 = [0]*m
for i in range(m):
x1[i] = x[i]
y1[i] = y[i]
a = random.randint(0,m-1) #随机产生一个点
for i in range(a): #交叉运算
x1[i]=y[i]
y1[i]=x[i]
ob = 0
for i in range(m): #判断交叉的合法性并进行维护，直到交叉成功
if(i<a):
ob = find(x1[i],x1,a)
while(ob != -1):#x1[i]与x[ob]重复
x1[i] = y1[ob]#y1[ob]即y[ob],必与x[ob](x1[ob])不同,将之赋给x1[i]，出现不重复概率更高
ob = find(x1[i],x1,a)#再次检测
for i in range(m): #操作同上，维护另一新个体的交叉合法性。
if(i<a):
ob = find(y1[i],y1,a)
while(ob != -1):
y1[i] = x1[ob]
ob = find(y1[i],y1,a)
for i in range(m):
x[i]=x1[i]
y[i]=y1[i]
def slove(choice): # 总执行函数
global best
for i in range(n): # 选择
value[i] = get_value(dna[i]) # 计算距离
max_id = value.index(max(value)) # 记录id
min_id = value.index(min(value))
if(value[min_id] < best):
best = value[min_id]
for i in range(m):
way[i] = dna[min_id][i]
value[max_id] = value[min_id] # 最优保存策略:最优解覆盖最差解
fa = -1
for i in range(m):
dna[max_id][i] = dna[min_id][i]
for i in range(n): # 交叉
if(random.random()> pc or i == max_id or i == min_id):
continue
if(fa == -1): # fa的判断让每两个样本才能有一次交叉操作
fa = i
else :
if(choice==1):
cross1(dna[fa], dna[i]) # 均匀交叉
if(choice==2):
cross2(dna[fa], dna[i]) # 多点交叉
if(choice==3):
cross3(dna[fa], dna[i]) # 一点交叉
fa = -1
for i in range(n): #变异运算
if(random.random()> pm or i == max_id or i == min_id):
continue
mutation(dna[i])
def main(pc_=0.9,pm_=0.2,n_=10,cross=1,epochs=10000):
global n,m,pc,pm,best,p,dna,value,way
p = []
pc = pc_
pm = pm_ #pc为交配概率 pm为变异概率
best = 1000 #best记录最优距离，初始化无限大
n = n_ #n:种群样本个数
m = 30 #m:城市个数
dna = [[0]*(m) for i in range(n)] #开辟n*m的数组
value = [0.0]*n #value数组记录个体适应度
way = [0]*m #way数组记录最优解路线
mycol() #数据输入
init() #群体初始化
for i in range(epochs): #控制进化次数
slove(cross)
print("The distance is",round(best,2)) #打印距离
#draw(way) # 画图描绘路线
#print("The way is",way) # 打印路线，以序列表示
#print("************************")
return best
if __name__ == "__main__":
k = 0
#main(epochs=200)
if(k==1):
"""不同交叉方法的性能表现"""
way_cross = []
distance1 = []
distance2 = []
distance3 = []
for i in range(1,16):
print(i)
epoch = i*1000
way_cross.append(epoch)
print("一点交叉法",end=' ')
distance1.append(main(cross=1,epochs=epoch)) # 一点交叉
print("多点交叉法",end=' ')
distance2.append(main(cross=2,epochs=epoch)) # 多点交叉
print("均匀交叉法",end=' ')
distance3.append(main(cross=3,epochs=epoch)) # 均匀交叉
plt.plot(way_cross,distance1,color='green',label='One-point Crossing')
plt.plot(way_cross,distance2,color='blue',label='Multi-point Crossing')
plt.plot(way_cross,distance3,color='red',label='Uniform Crossing')
plt.plot(way_cross,distance1,color='green')
plt.plot(way_cross,distance2,color='blue')
plt.plot(way_cross,distance3,color='red')
plt.xlabel('way_cross')
plt.ylabel('distance')
plt.title('The effect of the way of cross on the distance')
plt.legend()
elif(k==2):
"""不同交叉概率的性能表现"""
cross_probability = []
distance = []
for i in range(1,5):
print("the probability of cross is %.1f"%(i*0.2))
cross_probability.append(i*0.2)
distance.append(main(pc_=i*0.2,epochs=40000))
plt.plot(cross_probability,distance)
plt.xlabel('cross_probability')
plt.ylabel('distance')
plt.title('The effect of the probability of cross on the distance')
elif(k==3):
"""不同变异概率的性能表现"""
mutation_probability = []
distance = []
for i in range(1,10):
print("the probability of mutation is %.1f"%(i*0.1))
mutation_probability.append(i*0.1)
distance.append(main(pc_=i*0.1,epochs=30000))
plt.plot(mutation_probability,distance)
plt.xlabel('mutation_probability')
plt.ylabel('distance')
plt.title('The effect of the probability of mutation on the distance')
elif(k==4):
"""不同样本个数的性能表现"""
sample_numbers = []
distance = []
for i in range(2,10):
print("the numbers of sample is %d"%(i*2))
sample_numbers.append(i*2)
distance.append(main(n_=i*2))
plt.plot(sample_numbers,distance)
plt.xlabel('sample_numbers')
plt.ylabel('distance')
plt.title('The effect of the number of sample on the distance')
elif(k==5):
"""不同迭代次数的性能表现"""
epopchs = []
distance = []
for i in range(1):
print("epochs = %d"%(i*3000+1000))
epopchs.append(i*3000+1000)
distance.append(main(epochs=i*3000+1000))
plt.plot(epopchs,distance)
plt.xlabel('epochs')
plt.ylabel('distance')
plt.title('The effect of the number of epochs on the distance')
elif(k==6):
'''遗传算法与禁忌搜索算法的比较'''
from 禁忌TSP import TSA_TSP
way_cross = []
distance1 = []
distance2 = []
i = 500
epoch = [x for x in range(1,i+1)]
#distance1.append(main(epochs=epoch))
#distance2.append(comparasion(epoch))
plt.plot(epoch,GA_TSP(epochs=i),color='blue',label='GA')
plt.plot(epoch,TSA_TSP(i),color='red',label='TSA')
plt.xlabel('epochs')
plt.ylabel('distance')
plt.title('The effect of the difference methods on the distance')
#plt.legend()
#plt.show()
main()
city.py:
china = {
"海门": [121.15, 31.89],
"鄂尔多斯": [109.781327, 39.608266],
"招远": [120.38, 37.35],
"舟山": [122.207216, 29.985295],
"齐齐哈尔": [123.97, 47.33],
"盐城": [120.13, 33.38],
"赤峰": [118.87, 42.28],
"青岛": [120.33, 36.07],
"乳山": [121.52, 36.89],
"金昌": [102.188043, 38.520089],
"泉州": [118.58, 24.93],
"莱西": [120.53, 36.86],
"日照": [119.46, 35.42],
"胶南": [119.97, 35.88],
"南通": [121.05, 32.08],
"拉萨": [91.11, 29.97],
"云浮": [112.02, 22.93],
"梅州": [116.1, 24.55],
"文登": [122.05, 37.2],
"上海": [121.48, 31.22],
"攀枝花": [101.718637, 26.582347],
"威海": [122.1, 37.5],
"承德": [117.93, 40.97],
"厦门": [118.1, 24.46],
"汕尾": [115.375279, 22.786211],
"潮州": [116.63, 23.68],
"丹东": [124.37, 40.13],
"太仓": [121.1, 31.45],
"曲靖": [103.79, 25.51],
"烟台": [121.39, 37.52],
"福州": [119.3, 26.08],
"瓦房店": [121.979603, 39.627114],
"即墨": [120.45, 36.38],
"抚顺": [123.97, 41.97],
"玉溪": [102.52, 24.35],
"张家口": [114.87, 40.82],
"阳泉": [113.57, 37.85],
"莱州": [119.942327, 37.177017],
"湖州": [120.1, 30.86],
"汕头": [116.69, 23.39],
"昆山": [120.95, 31.39],
"宁波": [121.56, 29.86],
"湛江": [110.359377, 21.270708],
"揭阳": [116.35, 23.55],
"荣成": [122.41, 37.16],
"连云港": [119.16, 34.59],
"葫芦岛": [120.836932, 40.711052],
"常熟": [120.74, 31.64],
"东莞": [113.75, 23.04],
"河源": [114.68, 23.73],
"淮安": [119.15, 33.5],
"泰州": [119.9, 32.49],
"南宁": [108.33, 22.84],
"营口": [122.18, 40.65],
"惠州": [114.4, 23.09],
"江阴": [120.26, 31.91],
"蓬莱": [120.75, 37.8],
"韶关": [113.62, 24.84],
"嘉峪关": [98.289152, 39.77313],
"广州": [113.23, 23.16],
"延安": [109.47, 36.6],
"太原": [112.53, 37.87],
"清远": [113.01, 23.7],
"中山": [113.38, 22.52],
"昆明": [102.73, 25.04],
"寿光": [118.73, 36.86],
"盘锦": [122.070714, 41.119997],
"长治": [113.08, 36.18],
"深圳": [114.07, 22.62],
"珠海": [113.52, 22.3],
"宿迁": [118.3, 33.96],
"咸阳": [108.72, 34.36],
"铜川": [109.11, 35.09],
"平度": [119.97, 36.77],
"佛山": [113.11, 23.05],
"海口": [110.35, 20.02],
"江门": [113.06, 22.61],
"章丘": [117.53, 36.72],
"肇庆": [112.44, 23.05],
"大连": [121.62, 38.92],
"临汾": [111.5, 36.08],
"吴江": [120.63, 31.16],
"石嘴山": [106.39, 39.04],
"沈阳": [123.38, 41.8],
"苏州": [120.62, 31.32],
"茂名": [110.88, 21.68],
"嘉兴": [120.76, 30.77],
"长春": [125.35, 43.88],
"胶州": [120.03336, 36.264622],
"银川": [106.27, 38.47],
"张家港": [120.555821, 31.875428],
"三门峡": [111.19, 34.76],
"锦州": [121.15, 41.13],
"南昌": [115.89, 28.68],
"柳州": [109.4, 24.33],
"三亚": [109.511909, 18.252847],
"自贡": [104.778442, 29.33903],
"吉林": [126.57, 43.87],
"阳江": [111.95, 21.85],
"泸州": [105.39, 28.91],
"西宁": [101.74, 36.56],
"宜宾": [104.56, 29.77],
"呼和浩特": [111.65, 40.82],
"成都": [104.06, 30.67],
"大同": [113.3, 40.12],
"镇江": [119.44, 32.2],
"桂林": [110.28, 25.29],
"张家界": [110.479191, 29.117096],
"宜兴": [119.82, 31.36],
"北海": [109.12, 21.49],
"西安": [108.95, 34.27],
"金坛": [119.56, 31.74],
"东营": [118.49, 37.46],
"牡丹江": [129.58, 44.6],
"遵义": [106.9, 27.7],
"绍兴": [120.58, 30.01],
"扬州": [119.42, 32.39],
"常州": [119.95, 31.79],
"潍坊": [119.1, 36.62],
"重庆": [106.54, 29.59],
"台州": [121.420757, 28.656386],
"南京": [118.78, 32.04],
"滨州": [118.03, 37.36],
"贵阳": [106.71, 26.57],
"无锡": [120.29, 31.59],
"本溪": [123.73, 41.3],
"克拉玛依": [84.77, 45.59],
"渭南": [109.5, 34.52],
"马鞍山": [118.48, 31.56],
"宝鸡": [107.15, 34.38],
"焦作": [113.21, 35.24],
"句容": [119.16, 31.95],
"北京": [116.46, 39.92],
"徐州": [117.2, 34.26],
"衡水": [115.72, 37.72],
"包头": [110, 40.58],
"绵阳": [104.73, 31.48],
"乌鲁木齐": [87.68, 43.77],
"枣庄": [117.57, 34.86],
"杭州": [120.19, 30.26],
"淄博": [118.05, 36.78],
"鞍山": [122.85, 41.12],
"溧阳": [119.48, 31.43],
"库尔勒": [86.06, 41.68],
"安阳": [114.35, 36.1],
"开封": [114.35, 34.79],
"济南": [117, 36.65],
"德阳": [104.37, 31.13],
"温州": [120.65, 28.01],
"九江": [115.97, 29.71],
"邯郸": [114.47, 36.6],
"临安": [119.72, 30.23],
"兰州": [103.73, 36.03],
"沧州": [116.83, 38.33],
"临沂": [118.35, 35.05],
"南充": [106.110698, 30.837793],
"天津": [117.2, 39.13],
"富阳": [119.95, 30.07],
"泰安": [117.13, 36.18],
"诸暨": [120.23, 29.71],
"郑州": [113.65, 34.76],
"哈尔滨": [126.63, 45.75],
"聊城": [115.97, 36.45],
"芜湖": [118.38, 31.33],
"唐山": [118.02, 39.63],
"平顶山": [113.29, 33.75],
"邢台": [114.48, 37.05],
"德州": [116.29, 37.45],
"济宁": [116.59, 35.38],
"荆州": [112.239741, 30.335165],
"宜昌": [111.3, 30.7],
"义乌": [120.06, 29.32],
"丽水": [119.92, 28.45],
"洛阳": [112.44, 34.7],
"秦皇岛": [119.57, 39.95],
"株洲": [113.16, 27.83],
"石家庄": [114.48, 38.03],
"莱芜": [117.67, 36.19],
"常德": [111.69, 29.05],
"保定": [115.48, 38.85],
"湘潭": [112.91, 27.87],
"金华": [119.64, 29.12],
"岳阳": [113.09, 29.37],
"长沙": [113, 28.21],
"衢州": [118.88, 28.97],
"廊坊": [116.7, 39.53],
"菏泽": [115.480656, 35.23375],
"合肥": [117.27, 31.86],
"武汉": [114.31, 30.52],
"大庆": [125.03, 46.58],
"台湾": [120.96, 23.70],
"香港": [114.11, 22.40],
"澳门": [113.54, 22.20]
}
#193个国内城市,198个国外城市
world = {
"阿富汗": [67.709953, 33.93911],
"安哥拉": [17.873887, -11.202692],
"阿尔巴尼亚": [20.168331, 41.153332],
"阿联酋": [53.847818, 23.424076],
"阿根廷": [-63.61667199999999, -38.416097],
"亚美尼亚": [45.038189, 40.069099],
"法属南半球和南极领地": [69.348557, -49.280366],
"澳大利亚": [133.775136, -25.274398],
"奥地利": [14.550072, 47.516231],
"阿塞拜疆": [47.576927, 40.143105],
"布隆迪": [29.918886, -3.373056],
"比利时": [4.469936, 50.503887],
"贝宁": [2.315834, 9.30769],
"布基纳法索": [-1.561593, 12.238333],
"孟加拉国": [90.356331, 23.684994],
"保加利亚": [25.48583, 42.733883],
"巴哈马": [-77.39627999999999, 25.03428],
"波斯尼亚和黑塞哥维那": [17.679076, 43.915886],
"白俄罗斯": [27.953389, 53.709807],
"伯利兹": [-88.49765, 17.189877],
"百慕大": [-64.7505, 32.3078],
"玻利维亚": [-63.58865299999999, -16.290154],
"巴西": [-51.92528, -14.235004],
"文莱": [114.727669, 4.535277],
"不丹": [90.433601, 27.514162],
"博茨瓦纳": [24.684866, -22.328474],
"中非共和国": [20.939444, 6.611110999999999],
"加拿大": [-106.346771, 56.130366],
"瑞士": [8.227511999999999, 46.818188],
"智利": [-71.542969, -35.675147],
"中国": [104.195397, 35.86166],
"象牙海岸": [-5.547079999999999, 7.539988999999999],
"喀麦隆": [12.354722, 7.369721999999999],
"刚果民主共和国": [21.758664, -4.038333],
"刚果共和国": [15.827659, -0.228021],
"哥伦比亚": [-74.297333, 4.570868],
"哥斯达黎加": [-83.753428, 9.748916999999999],
"古巴": [-77.781167, 21.521757],
"北塞浦路斯": [33.429859, 35.126413],
"塞浦路斯": [33.429859, 35.126413],
"捷克共和国": [15.472962, 49.81749199999999],
"德国": [10.451526, 51.165691],
"吉布提": [42.590275, 11.825138],
"丹麦": [9.501785, 56.26392],
"多明尼加共和国": [-70.162651, 18.735693],
"阿尔及利亚": [1.659626, 28.033886],
"厄瓜多尔": [-78.18340599999999, -1.831239],
"埃及": [30.802498, 26.820553],
"厄立特里亚": [39.782334, 15.179384],
"西班牙": [-3.74922, 40.46366700000001],
"爱沙尼亚": [25.013607, 58.595272],
"埃塞俄比亚": [40.489673, 9.145000000000001],
"芬兰": [25.748151, 61.92410999999999],
"斐": [178.065032, -17.713371],
"福克兰群岛": [-59.523613, -51.796253],
"法国": [2.213749, 46.227638],
"加蓬": [11.609444, -0.803689],
"英国": [-3.435973, 55.378051],
"格鲁吉亚": [-82.9000751, 32.1656221],
"加纳": [-1.023194, 7.946527],
"几内亚": [-9.696645, 9.945587],
"冈比亚": [-15.310139, 13.443182],
"几内亚比绍": [-15.180413, 11.803749],
"赤道几内亚": [10.267895, 1.650801],
"希腊": [21.824312, 39.074208],
"格陵兰": [-42.604303, 71.706936],
"危地马拉": [-90.23075899999999, 15.783471],
"法属圭亚那": [-53.125782, 3.933889],
"圭亚那": [-58.93018, 4.860416],
"洪都拉斯": [-86.241905, 15.199999],
"克罗地亚": [15.2, 45.1],
"海地": [-72.285215, 18.971187],
"匈牙利": [19.503304, 47.162494],
"印尼": [113.921327, -0.789275],
"印度": [78.96288, 20.593684],
"爱尔兰": [-8.24389, 53.41291],
"伊朗": [53.688046, 32.427908],
"伊拉克": [43.679291, 33.223191],
"冰岛": [-19.020835, 64.963051],
"以色列": [34.851612, 31.046051],
"意大利": [12.56738, 41.87194],
"牙买加": [-77.297508, 18.109581],
"约旦": [36.238414, 30.585164],
"日本": [138.252924, 36.204824],
"哈萨克斯坦": [66.923684, 48.019573],
"肯尼亚": [37.906193, -0.023559],
"吉尔吉斯斯坦": [74.766098, 41.20438],
"柬埔寨": [104.990963, 12.565679],
"韩国": [127.766922, 35.907757],
"科索沃": [20.902977, 42.6026359],
"科威特": [47.481766, 29.31166],
"老挝": [102.495496, 19.85627],
"黎巴嫩": [35.862285, 33.854721],
"利比里亚": [-9.429499000000002, 6.428055],
"利比亚": [17.228331, 26.3351],
"斯里兰卡": [80.77179699999999, 7.873053999999999],
"莱索托": [28.233608, -29.609988],
"立陶宛": [23.881275, 55.169438],
"卢森堡": [6.129582999999999, 49.815273],
"拉脱维亚": [24.603189, 56.879635],
"摩洛哥": [-7.092619999999999, 31.791702],
"摩尔多瓦": [28.369885, 47.411631],
"马达加斯加": [46.869107, -18.766947],
"墨西哥": [-102.552784, 23.634501],
"马其顿": [21.745275, 41.608635],
"马里": [-3.996166, 17.570692],
"缅甸": [95.956223, 21.913965],
"黑山": [19.37439, 42.708678],
"蒙古": [103.846656, 46.862496],
"莫桑比克": [35.529562, -18.665695],
"毛里塔尼亚": [-10.940835, 21.00789],
"马拉维": [34.301525, -13.254308],
"马来西亚": [101.975766, 4.210484],
"纳米比亚": [18.49041, -22.95764],
"新喀里多尼亚": [165.618042, -20.904305],
"尼日尔": [8.081666, 17.607789],
"尼日利亚": [8.675277, 9.081999],
"尼加拉瓜": [-85.207229, 12.865416],
"荷兰": [5.291265999999999, 52.132633],
"挪威": [8.468945999999999, 60.47202399999999],
"尼泊尔": [84.12400799999999, 28.394857],
"新西兰": [174.885971, -40.900557],
"阿曼": [55.923255, 21.512583],
"巴基斯坦": [69.34511599999999, 30.375321],
"巴拿马": [-80.782127, 8.537981],
"秘鲁": [-75.015152, -9.189967],
"菲律宾": [121.774017, 12.879721],
"巴布亚新几内亚": [143.95555, -6.314992999999999],
"波兰": [19.145136, 51.919438],
"波多黎各": [-66.590149, 18.220833],
"北朝鲜": [127.510093, 40.339852],
"葡萄牙": [-8.224454, 39.39987199999999],
"巴拉圭": [-58.443832, -23.442503],
"卡塔尔": [51.183884, 25.354826],
"罗马尼亚": [24.96676, 45.943161],
"俄罗斯": [105.318756, 61.52401],
"卢旺达": [29.873888, -1.940278],
"西撒哈拉": [-12.885834, 24.215527],
"沙特阿拉伯": [45.079162, 23.885942],
"苏丹": [30.217636, 12.862807],
"南苏丹": [31.3069788, 6.876991899999999],
"塞内加尔": [-14.452362, 14.497401],
"所罗门群岛": [160.156194, -9.64571],
"塞拉利昂": [-11.779889, 8.460555],
"萨尔瓦多": [-88.89653, 13.794185],
"索马里兰": [46.8252838, 9.411743399999999],
"索马里": [46.199616, 5.152149],
"塞尔维亚共和国": [21.005859, 44.016521],
"苏里南": [-56.027783, 3.919305],
"斯洛伐克": [19.699024, 48.669026],
"斯洛文尼亚": [14.995463, 46.151241],
"瑞典": [18.643501, 60.12816100000001],
"斯威士兰": [31.465866, -26.522503],
"叙利亚": [38.996815, 34.80207499999999],
"乍得": [18.732207, 15.454166],
"多哥": [0.824782, 8.619543],
"泰国": [100.992541, 15.870032],
"塔吉克斯坦": [71.276093, 38.861034],
"土库曼斯坦": [59.556278, 38.969719],
"东帝汶": [125.727539, -8.874217],
"特里尼达和多巴哥": [-61.222503, 10.691803],
"突尼斯": [9.537499, 33.886917],
"土耳其": [35.243322, 38.963745],
"坦桑尼亚联合共和国": [34.888822, -6.369028],
"乌干达": [32.290275, 1.373333],
"乌克兰": [31.16558, 48.379433],
"乌拉圭": [-55.765835, -32.522779],
"美国": [-95.712891, 37.09024],
"乌兹别克斯坦": [64.585262, 41.377491],
"委内瑞拉": [-66.58973, 6.42375],
"越南": [108.277199, 14.058324],
"瓦努阿图": [166.959158, -15.376706],
"西岸": [35.3027226, 31.9465703],
"也门": [48.516388, 15.552727],
"南非": [22.937506, -30.559482],
"赞比亚": [27.849332, -13.133897],
"津巴布韦": [29.154857, -19.015438]
}
