到某個節點最近距離 最短路徑當前節點的父節點

完整代碼

 1 #include <iostream>
 2 #include <string>
 3 #include <utility>
 4 #include <vector>
 5 #include <deque>
 6 #include <boost/graph/adjacency_list.hpp>
 7 //A*尋路算法
 8 #include <boost\graph\astar_search.hpp>
 9 //dijsktra
 

10 #include <boost\graph\dijkstra_shortest_paths.hpp>
11 //bellman-Ford算法
12 #include <boost\graph\bellman_ford_shortest_paths.hpp>
13 using namespace std;
14 using namespace boost;
15 
16 
17 void main()
18 {
19     //定義節點和邊的相關對象和屬性
20     enum { u, v, x, y, z, N };
21     char name[] = { ‘u
 
‘, ‘v‘, ‘x‘, ‘y‘, ‘z‘ };
22     typedef std::pair < int, int >E;
23     E edge_array[] = { E(u, y), E(u, x), E(u, v), E(v, u),
24         E(x, y), E(x, v), E(y, v), E(y, z), E(z, u), E(z, x) };
25     int weights[] = { -4, 8, 5, -2, 9, -3, 7, 2, 6, 7 };
26     int num_arcs = sizeof(edge_array) / sizeof
 
(E);
27 
28     //定義所用的圖種類和相關類型
29     typedef adjacency_list < vecS, vecS, directedS,
30         no_property, property < edge_weight_t, int > > Graph;
31     //生成圖對象
32     Graph g(edge_array, edge_array + num_arcs, weights, N);
33     graph_traits < Graph >::edge_iterator ei, ei_end;
34 
35     //distance用於放置依近到遠的路徑距離
36     std::vector<int> distance(N, (std::numeric_limits < short >::max)());
37     //parent用於放置最短路徑生成樹的各個頂點的父節點
38     std::vector<std::size_t> parent(N);
39     for (int i = 0; i < N; ++i)
40         parent[i] = i;
41 
42 
43     //將源點z對應距離設為0
44     distance[z] = 0;
45     //應用Bellman-Ford算法
46     bool r = bellman_ford_shortest_paths
47     (g, int(N), weight_map(get(edge_weight, g)).distance_map(&distance[0]).
48         predecessor_map(&parent[0]));
49 
50     if (r)
51     {
52         std::cout << "源點z到各點的最短路徑\n";
53         std::cout << "目的點\t" << "最短路徑\t" << "最短路徑樹的父節點:\n";
54         for (int i = 0; i < N; ++i)
55             std::cout << name[i] << ": \t" << distance[i]
56             << "\t\t" << name[parent[i]] << std::endl;
57     }
58     else
59         std::cout << "negative cycle" << std::endl;
60     
61     system("pause");
62 }

18.boost Ford最短路徑算法(效率低)