Python旅行商贪婪算法

2024-03-29 02:19:37 发布

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所以我为我的旅行推销员问题创建了一个排序,我可以按x坐标和y坐标排序。

我试图实现一个贪婪的搜索,但无法。

此外,每个点在矩阵城市中实例化,例如[0,3,4],其中0是标题,3是x坐标,4是y坐标。

这是我的程序,你应该可以运行。主要的问题是我的算法不起作用,我需要帮助将其修复为一个有效的贪婪算法。你可以在代码末尾找到算法。

http://pastebin.com/ABQ3x0PG

这是您需要的文本文件,它从中获取输入。

http://pastebin.com/c1UQzqEB


Tags: 实例代码程序com算法http标题排序
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1楼 · 发布于 2024-03-29 02:19:37

旅行商问题(TSP)是一个组合优化问题 问题,在给定地图(一组城市及其位置)的情况下 想找到一个访问所有城市的命令 旅行距离最小。

我建议先解决tsp问题,然后再解决视觉问题。

以下代码包含一组要演示的函数: -TSP的构造启发式算法 -一种改进的启发式算法 -本地搜索,随机启动本地搜索。

import math
import random


def distL2((x1,y1), (x2,y2)):
    """Compute the L2-norm (Euclidean) distance between two points.

    The distance is rounded to the closest integer, for compatibility
    with the TSPLIB convention.

    The two points are located on coordinates (x1,y1) and (x2,y2),
    sent as parameters"""
    xdiff = x2 - x1
    ydiff = y2 - y1
    return int(math.sqrt(xdiff*xdiff + ydiff*ydiff) + .5)


def distL1((x1,y1), (x2,y2)):
    """Compute the L1-norm (Manhattan) distance between two points.

    The distance is rounded to the closest integer, for compatibility
    with the TSPLIB convention.

    The two points are located on coordinates (x1,y1) and (x2,y2),
    sent as parameters"""
    return int(abs(x2-x1) + abs(y2-y1)+.5)


def mk_matrix(coord, dist):
    """Compute a distance matrix for a set of points.

    Uses function 'dist' to calculate distance between
    any two points.  Parameters:
    -coord -- list of tuples with coordinates of all points, [(x1,y1),...,(xn,yn)]
    -dist -- distance function
    """
    n = len(coord)
    D = {}      # dictionary to hold n times n matrix
    for i in range(n-1):
        for j in range(i+1,n):
            (x1,y1) = coord[i]
            (x2,y2) = coord[j]
            D[i,j] = dist((x1,y1), (x2,y2))
            D[j,i] = D[i,j]
    return n,D

def read_tsplib(filename):
    "basic function for reading a TSP problem on the TSPLIB format"
    "NOTE: only works for 2D euclidean or manhattan distances"
    f = open(filename, 'r');

    line = f.readline()
    while line.find("EDGE_WEIGHT_TYPE") == -1:
        line = f.readline()

    if line.find("EUC_2D") != -1:
        dist = distL2
    elif line.find("MAN_2D") != -1:
        dist = distL1
    else:
        print "cannot deal with non-euclidean or non-manhattan distances"
        raise Exception

    while line.find("NODE_COORD_SECTION") == -1:
        line = f.readline()

    xy_positions = []
    while 1:
        line = f.readline()
        if line.find("EOF") != -1: break
        (i,x,y) = line.split()
        x = float(x)
        y = float(y)
        xy_positions.append((x,y))

    n,D = mk_matrix(xy_positions, dist)
    return n, xy_positions, D


def mk_closest(D, n):
    """Compute a sorted list of the distances for each of the nodes.

    For each node, the entry is in the form [(d1,i1), (d2,i2), ...]
    where each tuple is a pair (distance,node).
    """
    C = []
    for i in range(n):
        dlist = [(D[i,j], j) for j in range(n) if j != i]
        dlist.sort()
        C.append(dlist)
    return C


def length(tour, D):
    """Calculate the length of a tour according to distance matrix 'D'."""
    z = D[tour[-1], tour[0]]    # edge from last to first city of the tour
    for i in range(1,len(tour)):
        z += D[tour[i], tour[i-1]]      # add length of edge from city i-1 to i
    return z


def randtour(n):
    """Construct a random tour of size 'n'."""
    sol = range(n)      # set solution equal to [0,1,...,n-1]
    random.shuffle(sol) # place it in a random order
    return sol


def nearest(last, unvisited, D):
    """Return the index of the node which is closest to 'last'."""
    near = unvisited[0]
    min_dist = D[last, near]
    for i in unvisited[1:]:
        if D[last,i] < min_dist:
            near = i
            min_dist = D[last, near]
    return near


def nearest_neighbor(n, i, D):
    """Return tour starting from city 'i', using the Nearest Neighbor.

    Uses the Nearest Neighbor heuristic to construct a solution:
    - start visiting city i
    - while there are unvisited cities, follow to the closest one
    - return to city i
    """
    unvisited = range(n)
    unvisited.remove(i)
    last = i
    tour = [i]
    while unvisited != []:
        next = nearest(last, unvisited, D)
        tour.append(next)
        unvisited.remove(next)
        last = next
    return tour



def exchange_cost(tour, i, j, D):
    """Calculate the cost of exchanging two arcs in a tour.

    Determine the variation in the tour length if
    arcs (i,i+1) and (j,j+1) are removed,
    and replaced by (i,j) and (i+1,j+1)
    (note the exception for the last arc).

    Parameters:
    -t -- a tour
    -i -- position of the first arc
    -j>i -- position of the second arc
    """
    n = len(tour)
    a,b = tour[i],tour[(i+1)%n]
    c,d = tour[j],tour[(j+1)%n]
    return (D[a,c] + D[b,d]) - (D[a,b] + D[c,d])


def exchange(tour, tinv, i, j):
    """Exchange arcs (i,i+1) and (j,j+1) with (i,j) and (i+1,j+1).

    For the given tour 't', remove the arcs (i,i+1) and (j,j+1) and
    insert (i,j) and (i+1,j+1).

    This is done by inverting the sublist of cities between i and j.
    """
    n = len(tour)
    if i>j:
        i,j = j,i
    assert i>=0 and i<j-1 and j<n
    path = tour[i+1:j+1]
    path.reverse()
    tour[i+1:j+1] = path
    for k in range(i+1,j+1):
        tinv[tour[k]] = k


def improve(tour, z, D, C):
    """Try to improve tour 't' by exchanging arcs; return improved tour length.

    If possible, make a series of local improvements on the solution 'tour',
    using a breadth first strategy, until reaching a local optimum.
    """
    n = len(tour)
    tinv = [0 for i in tour]
    for k in range(n):
        tinv[tour[k]] = k  # position of each city in 't'
    for i in range(n):
        a,b = tour[i],tour[(i+1)%n]
        dist_ab = D[a,b]
        improved = False
        for dist_ac,c in C[a]:
            if dist_ac >= dist_ab:
                break
            j = tinv[c]
            d = tour[(j+1)%n]
            dist_cd = D[c,d]
            dist_bd = D[b,d]
            delta = (dist_ac + dist_bd) - (dist_ab + dist_cd)
            if delta < 0:       # exchange decreases length
                exchange(tour, tinv, i, j);
                z += delta
                improved = True
                break
        if improved:
            continue
        for dist_bd,d in C[b]:
            if dist_bd >= dist_ab:
                break
            j = tinv[d]-1
            if j==-1:
                j=n-1
            c = tour[j]
            dist_cd = D[c,d]
            dist_ac = D[a,c]
            delta = (dist_ac + dist_bd) - (dist_ab + dist_cd)
            if delta < 0:       # exchange decreases length
                exchange(tour, tinv, i, j);
                z += delta
                break
    return z


def localsearch(tour, z, D, C=None):
    """Obtain a local optimum starting from solution t; return solution length.

    Parameters:
      tour -- initial tour
      z -- length of the initial tour
      D -- distance matrix
    """
    n = len(tour)
    if C == None:
        C = mk_closest(D, n)     # create a sorted list of distances to each node
    while 1:
        newz = improve(tour, z, D, C)
        if newz < z:
            z = newz
        else:
            break
    return z


def multistart_localsearch(k, n, D, report=None):
    """Do k iterations of local search, starting from random solutions.

    Parameters:
    -k -- number of iterations
    -D -- distance matrix
    -report -- if not None, call it to print verbose output

    Returns best solution and its cost.
    """
    C = mk_closest(D, n) # create a sorted list of distances to each node
    bestt=None
    bestz=None
    for i in range(0,k):
        tour = randtour(n)
        z = length(tour, D)
        z = localsearch(tour, z, D, C)
        if z < bestz or bestz == None:
            bestz = z
            bestt = list(tour)
            if report:
                report(z, tour)

    return bestt, bestz


if __name__ == "__main__":
    """Local search for the Travelling Saleman Problem: sample usage."""

    #
    # test the functions:
    #

    # random.seed(1)    # uncomment for having always the same behavior
    import sys
    if len(sys.argv) == 1:
        # create a graph with several cities' coordinates
        coord = [(4,0),(5,6),(8,3),(4,4),(4,1),(4,10),(4,7),(6,8),(8,1)]
        n, D = mk_matrix(coord, distL2) # create the distance matrix
        instance = "toy problem"
    else:
        instance = sys.argv[1]
        n, coord, D = read_tsplib(instance)     # create the distance matrix
        # n, coord, D = read_tsplib('INSTANCES/TSP/eil51.tsp')  # create the distance matrix

    # function for printing best found solution when it is found
    from time import clock
    init = clock()
    def report_sol(obj, s=""):
        print "cpu:%g\tobj:%g\ttour:%s" % \
              (clock(), obj, s)


    print "*** travelling salesman problem ***"
    print

    # random construction
    print "random construction + local search:"
    tour = randtour(n)     # create a random tour
    z = length(tour, D)     # calculate its length
    print "random:", tour, z, '  -->  ',   
    z = localsearch(tour, z, D)      # local search starting from the random tour
    print tour, z
    print

    # greedy construction
    print "greedy construction with nearest neighbor + local search:"
    for i in range(n):
        tour = nearest_neighbor(n, i, D)     # create a greedy tour, visiting city 'i' first
        z = length(tour, D)
        print "nneigh:", tour, z, '  -->  ',
        z = localsearch(tour, z, D)
        print tour, z
    print

    # multi-start local search
    print "random start local search:"
    niter = 100
    tour,z = multistart_localsearch(niter, n, D, report_sol)
    assert z == length(tour, D)
    print "best found solution (%d iterations): z = %g" % (niter, z)
    print tour

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