min_len = ant.path_len
return best_ant
def find_distance(cities, dists):
N = len(cities)
path_len = 0
for i in range(N):
if i == N - 1:
path_len += dists[cities[i]][cities[0]]
else:
path_len += dists[cities[i]][cities[i + 1]]
return path_len
def ant_solve(cities):
dists = get_dists(cities)
phero = find_distance(solve(cities), dists)
phers = get_phers(cities, len(cities) / phero)
fields = dict(cities=cities, dists=dists, phers=phers)
ants = get_ants(len(cities), fields)
best_ant = start(fields, ants)
return best_ant
if __name__ == '__main__':
assert len(sys.argv) > 1
solution = ant_solve(read_input(sys.argv[1]))
write_solution(solution.path)
#print_solution(solution.path)
def fullsearch(cities):
N = len(cities)
global min_length, min_path
min_length = 100000000
min_path = []
def fullsearch_sub(start, solution):
global min_length, min_path
dist = path_length(solution, cities)
if dist > min_length:
return
if N == len(solution):
new_len = path_length(solution, cities)
if(new_len < min_length):
min_length = new_len
min_path = solution[:]
else:
for current_city in range(N):
if current_city not in solution:
solution.append(current_city)
fullsearch_sub(current_city + 1, solution)
solution.pop()
for i in range(N):
fullsearch_sub(i, min_path)
return min_path
if __name__ == '__main__':
assert len(sys.argv) > 1
solution = fullsearch(read_input(sys.argv[1]))
write_solution(solution)
print_solution(solution)
probe = (base + 1) % N
next_probe = (probe + 1) % N
next_base = (base + 1) % N
delta = 0
for j in range(N-3):
if(count == 0):
if(distance(cities[base], cities[next_base]) > distance(cities[next_base], cities[next_probe])):
delta = distance(cities[base], cities[next_base]) - distance(cities[next_base], cities[next_probe])
cities = parcel_reverse(cities, next_base, probe)
count += 1
else:
if(distance(cities[base], cities[next_base]) + delta > distance(cities[next_base], cities[next_probe])):
count += 1
delta = distance(cities[base], cities[next_base]) + distance(cities[probe], cities[next_probe]) - distance(cities[base], cities[probe]) - distance(cities[next_base], cities[next_base]) + delta
cities = parcel_reverse(cities, next_base, probe)
probe = (probe + 1) % N
next_probe = (probe + 1) % N
return cities
if __name__ == '__main__':
assert len(sys.argv) > 1
city = read_input(sys.argv[1])
solution = solve(city)
solution = lin_kernighan(solution)
solution = do_2opt(solution)
solution = match_index(city, solution)
write_solution(solution)
#print_solution(solution)
def find_distance(cities, dists):
N = len(cities)
path_len = 0
for i in range(N):
if i == N - 1:
path_len += dists[cities[i]][cities[0]]
else:
path_len += dists[cities[i]][cities[i + 1]]
return path_len
def ant_solve(cities):
dists = get_dists(cities)
phero = find_distance(solve(cities), dists)
phers = get_phers(cities, len(cities)/phero)
fields = dict(
cities = cities,
dists = dists,
phers = phers
)
ants = get_ants(len(cities), fields)
best_ant = start(fields, ants)
return best_ant
if __name__ == '__main__':
assert len(sys.argv) > 1
solution = ant_solve(read_input(sys.argv[1]))
write_solution(solution.path)
#print_solution(solution.path)
