Traveling Salesman: Nearest Neighbor
March 16, 2010
We saw in the previous exercise that finding an exact solution for the traveling salesman problem is extremely time consuming, taking time O(n!). The alternative is a heuristic that delivers a reasonably good solution quickly. One such heuristic is the “nearest neighbor:” pick a starting point, then at each step pick the nearest unvisited point, add it to the current tour and mark it visited, repeating until there are no unvisited points.
Your task is to write a program that solves the traveling salesman problem using the nearest neighbor heuristic. When you are finished, you are welcome to read or run a suggested solution, or to post your own solution or discuss the exercise in the comments below.
Programming Praxis 
My Haskell solution (see http://bonsaicode.wordpress.com/2010/03/16/programming-praxis-traveling-salesman-nearest-neighbor/ for a version with comments):
import Control.Monad import Data.List import qualified Data.List.Key as K import System.Random dist :: (Int, Int) -> (Int, Int) -> Float dist (x1, y1) (x2, y2) = sqrt (f (x1 - x2) ** 2 + f (y1 - y2) ** 2) where f = fromIntegral cost :: [(Int, Int)] -> Float cost xs = sum $ zipWith dist xs (tail xs ++ xs) randomPoints :: Int -> IO [(Int, Int)] randomPoints n = f [] where f ps = if length ps == n then return ps else do p <- liftM2 (,) rnd rnd if elem p ps then f ps else f (p:ps) rnd = randomRIO (0, 10 * n) nearTour :: [(Int, Int)] -> [(Integer, (Int, Int))] nearTour = f . zip [0..] where f [] = [] f [x] = [x] f ((i,p):ps) = (i,p) : f (nxt : delete nxt ps) where nxt = K.minimum (dist p . snd) ps[…] Praxis – Traveling Salesman: Nearest Neighbor By Remco Niemeijer In today’s Programming Praxis exercise we have to implement a significantly faster algorithm for the traveling […]
[…] data. On Programming Praxis they have proposed to resolve the problem using brute force, and using the closest neighbor (a simplification of the […]
A solution in Python, including comparison with brute force. Comments on my blog on http://wrongsideofmemphis.wordpress.com/2010/03/16/travelling-salesman/
import random
import itertools
import operator
import datetime
MAX_X = 100
MAX_Y = 100
def random_cities(number):
”’ Generate a number of cities located on random places ”’
cities = [ (random.randrange(0, MAX_X),
random.randrange(0, MAX_Y))
for i in range(number) ]
return cities
def path_lenght(path):
”’ Get the lenght of a path ”’
lenght = 0
for i in xrange(len(path) – 1):
# Add the distance between two cities
lenght += abs(complex(path[i][0], path[i][1])
– complex(path[i + 1][0], path[i + 1][1]))
return lenght
def find_path_bruteforce(cities):
”’ Find the smallest path using brute force ”’
lenghts = []
for path in itertools.permutations(cities, len(cities)):
# Get the length of the path, adding the returning point
total_path = path + (path[0],)
lenght = path_lenght(total_path)
lenghts.append((total_path, lenght))
# Get minimum
lenghts.sort(key=operator.itemgetter(1))
return lenghts[0]
def find_path_nearest(cities):
”’ Find the closest neibour ”’
lenghts = []
for city in cities:
lenght = 0
actual_cities = cities[:]
actual_city = actual_cities.pop(actual_cities.index(city))
path = [actual_city, ]
# Find nearest neibour
while actual_cities:
min_lenght = []
for next_city in actual_cities:
min_lenght.append((next_city, abs(complex(city[0], city[1])
– complex(next_city[0], next_city[1]))))
# Get closest neibor
min_lenght.sort(key=operator.itemgetter(1))
actual_city = min_lenght[0][0]
lenght += min_lenght[0][1]
actual_cities.pop(actual_cities.index(actual_city))
path.append(actual_city)
# Complete the trip with the first city
path.append(city)
lenghts.append((tuple(path), path_lenght(path)))
# Get minimum
lenghts.sort(key=operator.itemgetter(1))
return lenghts[0]
if __name__ == ‘__main__’:
for i in range(3, 10):
print ‘Number of cities: ‘, i
cities = random_cities(i)
time1 = datetime.datetime.now()
path2, lenght_neighbor = find_path_nearest(cities)
time2 = datetime.datetime.now()
print path2, lenght_neighbor
time_neighbor = time2 – time1
print ‘Time neighbor: ‘, time_neighbor
time1 = datetime.datetime.now()
path1, lenght_brute = find_path_bruteforce(cities)
time2 = datetime.datetime.now()
print path1, lenght_brute
time_brute = time2 – time1
print ‘Time brute force: ‘, time_brute
print ‘Diff: ‘, float(lenght_neighbor – lenght_brute) / lenght_brute * 100, ‘%’
Another brute force and nearest neighbor implementation in python.
from itertools import permutations from random import randint from timeit import timeit # complex numbers are used to represent cities because python has them built-in. # could easily be changed to 2-tuples by changing random_cities and leg_cost def random_cities(n,max_x=100,max_y=100): '''return set of n cities randomly placed on a max_x by max_y grid.''' cities = set() while len(cities) < n: cities.add(complex(randint(0,max_x),randint(0,max_y))) return cities def leg_cost(city1,city2): '''returns cost of travel from city1 to city2.''' return abs(city1 - city2) def path_cost(path): '''total cost to travel the path and return to starting city.''' return sum(leg_cost(path[n-1],path[n]) for n in range(len(path))) def brute_force(cities): '''finds shortest path by exhaustively checking all paths.''' return min(permutations(cities), key=path_cost) def nearest_neighbor(cities): '''finds a path through the cities using a nearest neighbor heuristic.''' unvisited = cities.copy() visited = [unvisited.pop()] while unvisited: city = min(unvisited, key=lambda c: leg_cost(visited[-1],c)) visited.append(city) unvisited.remove(city) return visited setup = "from __main__ import brute_force, nearest_neighbor, out" out = [ None ] for ncities in range(3,7): cities = random_cities(ncities) t_bf = timeit(stmt="out[0]=brute_force({0})".format(cities), setup=setup, number=1) c_bf = path_cost(out[0]) t_nn = timeit(stmt="out[0]=nearest_neighbor({0})".format(cities), setup=setup, number=1) c_nn = path_cost(out[0]) tdelta = 100*(t_nn - t_bf) / t_bf cdelta = 100*(c_nn - c_bf) / c_bf print """Number of cities: {ncities} time cost brute_force : {t_bf:8.1e} {c_bf:8.2} nearest_neighbor: {t_nn:8.1e} {c_nn:8.2} difference : {tdelta:8.1f}% {cdelta:8.1f}% """.format(**locals())hello
The algorithm I would like the C languagei
Can you help me?