import create as tc
import os_file as to
import numpy as np
import time
work_path = './work/'
nodes = 10
time.sleep(3)
while True:
cities_file = to.wait_msg_file(work_path, 'new_job.txt', delete=True)
cities = to.wait_np_file(work_path, cities_file, delete=False)
to.clean_work_path(work_path)
dist = tc.distance_matrix(cities)
to.set_np_file(work_path, 'distance_matrix.npy', dist)
num = len(cities)
population = 10
generations = []
print("generate the solution of routes.")
for i in range(nodes):
routes = tc.init_solutions(population, num)
generations.append(routes)
# hash_num=np.random.randint(100000,999999)
# file_name='solution_'+str(hash_num)+'.npy'
to.set_np_file(work_path, 'generations.npy', generations)
print("solution file setting over.waiting for results.")
# for i in range(population):
# hash_num=np.random.randint(100000,999999)
# file_name='solution_'+str(hash_num)+'.npy'
# to.set_np_file(work_path,file_name,np.array([solutions[i],9999999999999999,file_name]))
for i in range(num):
solutions.append(random_solution(cities))
return solutions
def mutation_1(solution):
n = len(solution)
start = np.random.randint(0, n - 2)
end = np.random.randint(start + 1, n)
k = solution[start:end]
solution[start:end] = k[::-1]
return solution
def mutation_2(solution):
n = len(solution)
start = np.random.randint(0, n - 2)
end = np.random.randint(start + 1, n)
k = solution[start]
solution[start:end - 1] = solution[start + 1:end]
solution[end - 1] = k
return solution
if __name__ == "__main__":
tos.clean_work_path(work_path)
cities = create_cities(100)
tos.set_np_file(work_path, 'cities.npy', cities)
with open(work_path + 'new_job.txt', 'w') as ff:
ff.write('cities.npy')
return s2
print("start work.")
while True:
generations=to.wait_np_file(work_path,'generations.npy',delete=True)
print("generation information getted. Now setting the gen_rdd.")
gen_rdd=sc.parallelize(generations)
print("rdd setted,now maping.")
res=gen_rdd.map(it.iteration)
print("mapping over,now reducing.")
res2=res.reduce(get_res)
print("The best solution is:",res2)
c=tc.cost(res2,m)
print("The lowest cost is :",c)
to.set_np_file(work_path,'final_solution.npy',[res2,c])
# msg=to.wait_np_file_start(work_path,'solution',delete=True)
# #print(msg)
# solution,c_orig,file_name=msg
# if file_name not in dic:
# dic[file_name]=[solution,c_orig,0]
# to.set_np_file(work_path,'cal_'+file_name[9:],msg)
# else:
# dic[file_name][:2]=[solution,c_orig]
# dic[file_name][2]+=1
# if dic[file_name][2]==10:
# #print("Get one final solution.")
# final_res.append(dic[file_name][:2])
import create as tc
import os_file as to
import numpy as np
import time
work_path='./work/'
m=to.wait_np_file(work_path,'distance_matrix.npy')
while True:
s_orig,c_orig,name=to.wait_np_file_start(work_path,'cal',delete=True)
s=tc.mutation_1(s_orig)
c=tc.cost(s,m)
if c<c_orig:
s_orig=s
c_orig=c
s=tc.mutation_2(s_orig)
c=tc.cost(s,m)
if c<c_orig:
s_orig=s
c_orig=c
res=np.array([s_orig,c_orig,name])
to.set_np_file(work_path,name,res)