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import numpy as np
import json
from math import radians, cos, sin, asin, sqrt
"""
@param:
CITIES the longitude and the latitude of cities
N_CITIES the number of cities
CROSS_RATE the rate of cross
MUTATE_RATE the rate of mutate
POP_SIZE the size of population
N_GENERATIONS the number of generations
"""
CITIES = np.array([[116.4, 39.9],
[117.2, 39.12],
[114.52, 38.05],
[112.55, 37.87],
[111.73, 40.83],
[123.43, 41.8],
[125.32, 43.9],
[126.53, 45.8],
[121.47, 31.23],
[118.78, 32.07],
[120.15, 30.28],
[117.25, 31.83],
[119.3, 26.08],
[115.85, 28.68],
[116.98, 36.67],
[113.62, 34.75],
[114.3, 30.6],
[112.93, 28.23],
[113.27, 23.13],
[108.37, 22.82],
[108.37, 22.82],
[106.55, 29.57],
[104.07, 30.67],
[106.63, 26.65],
[102.72, 25.05],
[91.13, 29.65],
[108.93, 34.27],
[103.82, 36.07],
[101.78, 36.62],
[106.28, 38.47],
[87.62, 43.82],
[121.52, 25.03],
[114.17, 22.28],
[113.55, 22.19]])
N_CITIES = CITIES.shape[0]
CROSS_RATE = 0.1
MUTATE_RATE = 0.02
POP_SIZE = 1000
N_GENERATIONS = 500
"""
Calculate the great circle distance between two points
on the earth (specified in decimal degrees)
@param:
lon1 Longitude of the first point
lat1 Latitude of the first point
lon2 Longitude of the second point
lat2 Latitude of the second point
"""
def haversine(lon1, lat1, lon2, lat2):
lon1, lat1, lon2, lat2 = map(radians, [lon1, lat1, lon2, lat2])
dlon = lon2 - lon1
dlat = lat2 - lat1
a = sin(dlat/2)**2 + cos(lat1) * cos(lat2) * sin(dlon/2)**2
c = 2 * asin(sqrt(a))
r = 6378.137
return c * r
class GA(object):
def __init__(self, DNA_size, cross_rate, mutation_rate, pop_size, ):
self.DNA_size = DNA_size
self.cross_rate = cross_rate
self.mutate_rate = mutation_rate
self.pop_size = pop_size
self.pop = np.vstack([np.random.permutation(DNA_size)
for _ in range(pop_size)])
def translateDNA(self, DNA, city_position):
line_x = np.empty_like(DNA, dtype=np.float64)
line_y = np.empty_like(DNA, dtype=np.float64)
for i, d in enumerate(DNA):
city_coord = city_position[d]
line_x[i, :] = city_coord[:, 0]
line_y[i, :] = city_coord[:, 1]
return line_x, line_y
def get_fitness(self, line_x, line_y):
total_distance = np.zeros((line_x.shape[0],), dtype=np.float64)
for i in range(line_x.shape[0]):
for j in range(line_x.shape[1]-1):
total_distance[i] += haversine(line_x[i][j],
line_y[i][j], line_x[i][j+1], line_y[i][j+1])
total_distance[i] += haversine(line_x[i][0], line_y[i][0],
line_x[i][line_x.shape[1]-1], line_y[i][line_x.shape[1]-1])
fitness = np.exp(self.DNA_size * 20000 / total_distance)
return fitness, total_distance
def select(self, fitness):
idx = np.random.choice(np.arange(
self.pop_size), size=self.pop_size, replace=True, p=fitness / fitness.sum())
return self.pop[idx]
def crossover(self, parent, pop):
if np.random.rand() < self.cross_rate:
i_ = np.random.randint(0, self.pop_size, size=1)
cross_points = np.random.randint(
0, 2, self.DNA_size).astype(np.bool)
keep_city = parent[~cross_points]
swap_city = pop[i_, np.in1d(
pop[i_].ravel(), keep_city, invert=True)]
parent[:] = np.concatenate((keep_city, swap_city))
return parent
def mutate(self, child):
for point in range(self.DNA_size):
if np.random.rand() < self.mutate_rate:
swap_point = np.random.randint(0, self.DNA_size)
swapA, swapB = child[point], child[swap_point]
child[point], child[swap_point] = swapB, swapA
return child
def evolve(self, fitness):
pop = self.select(fitness)
pop_copy = pop.copy()
for parent in pop:
child = self.crossover(parent, pop_copy)
child = self.mutate(child)
parent[:] = child
self.pop = pop
if __name__ == "__main__":
ga = GA(DNA_size=N_CITIES, cross_rate=CROSS_RATE,
mutation_rate=MUTATE_RATE, pop_size=POP_SIZE)
path_list = []
for generation in range(N_GENERATIONS):
lx, ly = ga.translateDNA(ga.pop, CITIES)
fitness, total_distance = ga.get_fitness(lx, ly)
ga.evolve(fitness)
best_idx = np.argmax(fitness)
print('Gen:', generation, '| total distance: %.4f' %
total_distance[best_idx],)
path_x = np.array(list(lx[best_idx]) + [lx[best_idx][0]])
path_y = np.array(list(ly[best_idx]) + [ly[best_idx][0]])
path = np.stack((path_x, path_y))
path = path.transpose(1, 0)
path_list.append(path.tolist())
f = open('data.json', 'w')
f.write(json.dumps(path_list))
f.close()
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