# Bidirectional Search

Searching a graph is quite famous problem and have a lot of practical use. We have already discussed here how to search for a goal vertex starting from a source vertex using BFS. In normal graph search using BFS/DFS we begin our search in one direction usually from source vertex toward the goal vertex, but what if we start search form both direction simultaneously.

Bidirectional search is a graph search algorithm which find smallest path form source to goal vertex. It runs two simultaneous search –

1. Forward search form source/initial vertex toward goal vertex
2. Backward search form goal/target vertex toward source vertex

Bidirectional search replaces single search graph(which is likely to grow exponentially) with two smaller sub graphs – one starting from initial vertex and other starting from goal vertex. The search terminates when two graphs intersect.

Just like A* algorithm, bidirectional search can be guided by a heuristic estimate of remaining distance from source to goal and vice versa for finding shortest path possible.

Consider following simple example- Suppose we want to find if there exists a path from vertex 0 to vertex 14. Here we can execute two searches, one from vertex 0 and other from vertex 14. When both forward and backward search meet at vertex 7, we know that we have found a path from node 0 to 14 and search can be terminated now. We can clearly see that we have successfully avoided unnecessary exploration.

Why bidirectional approach?

Because in many cases it is faster, it dramatically reduce the amount of required exploration.
Suppose if branching factor of tree is b and distance of goal vertex from source is d, then the normal BFS/DFS searching complexity would be . On the other hand, if we execute two search operation then the complexity would be for each search and total complexity would be which is far less than .

When to use bidirectional approach?

We can consider bidirectional approach when-

1. Both initial and goal states are unique and completely defined.
2. The branching factor is exactly the same in both directions.

Performance measures

• Completeness : Bidirectional search is complete if BFS is used in both searches.
• Optimality : It is optimal if BFS is used for search and paths have uniform cost.
• Time and Space Complexity : Time and space complexity is Below is very simple implementation representing the concept of bidirectional search using BFS. This implementation considers undirected paths without any weight.

 `// C++ program for Bidirectional BFS search ` `// to check path between two vertices ` `#include ` `using` `namespace` `std; ` ` `  `// class representing undirected graph ` `// using adjacency list ` `class` `Graph ` `{ ` `    ``//number of nodes in graph ` `    ``int` `V; ` ` `  `    ``// Adjacency list ` `    ``list<``int``> *adj; ` `public``: ` `    ``Graph(``int` `V); ` `    ``int` `isIntersecting(``bool` `*s_visited, ``bool` `*t_visited); ` `    ``void` `addEdge(``int` `u, ``int` `v); ` `    ``void` `printPath(``int` `*s_parent, ``int` `*t_parent, ``int` `s, ` `                             ``int` `t, ``int` `intersectNode); ` `    ``void` `BFS(list<``int``> *queue, ``bool` `*visited, ``int` `*parent); ` `    ``int` `biDirSearch(``int` `s, ``int` `t); ` `}; ` ` `  `Graph::Graph(``int` `V) ` `{ ` `    ``this``->V = V; ` `    ``adj = ``new` `list<``int``>[V]; ` `}; ` ` `  `// Method for adding undirected edge ` `void` `Graph::addEdge(``int` `u, ``int` `v) ` `{ ` `    ``this``->adj[u].push_back(v); ` `    ``this``->adj[v].push_back(u); ` `}; ` ` `  `// Method for Breadth First Search ` `void` `Graph::BFS(list<``int``> *queue, ``bool` `*visited, ` `                                    ``int` `*parent) ` `{ ` `    ``int` `current = queue->front(); ` `    ``queue->pop_front(); ` `    ``list<``int``>::iterator i; ` `    ``for` `(i=adj[current].begin();i != adj[current].end();i++) ` `    ``{ ` `        ``// If adjacent vertex is not visited earlier ` `        ``// mark it visited by assigning true value ` `        ``if` `(!visited[*i]) ` `        ``{ ` `            ``// set current as parent of this vertex ` `            ``parent[*i] = current; ` ` `  `            ``// Mark this vertex visited ` `            ``visited[*i] = ``true``; ` ` `  `            ``// Push to the end of queue ` `            ``queue->push_back(*i); ` `        ``} ` `    ``} ` `}; ` ` `  `// check for intersecting vertex ` `int` `Graph::isIntersecting(``bool` `*s_visited, ``bool` `*t_visited) ` `{ ` `    ``int` `intersectNode = -1; ` `    ``for``(``int` `i=0;i path; ` `    ``path.push_back(intersectNode); ` `    ``int` `i = intersectNode; ` `    ``while` `(i != s) ` `    ``{ ` `        ``path.push_back(s_parent[i]); ` `        ``i = s_parent[i]; ` `    ``} ` `    ``reverse(path.begin(), path.end()); ` `    ``i = intersectNode; ` `    ``while``(i != t) ` `    ``{ ` `        ``path.push_back(t_parent[i]); ` `        ``i = t_parent[i]; ` `    ``} ` ` `  `    ``vector<``int``>::iterator it; ` `    ``cout<<``"*****Path*****\n"``; ` `    ``for``(it = path.begin();it != path.end();it++) ` `        ``cout<<*it<<``" "``; ` `    ``cout<<``"\n"``; ` `}; ` ` `  `// Method for bidirectional searching ` `int` `Graph::biDirSearch(``int` `s, ``int` `t) ` `{ ` `    ``// boolean array for BFS started from ` `    ``// source and target(front and backward BFS) ` `    ``// for keeping track on visited nodes ` `    ``bool` `s_visited[V], t_visited[V]; ` ` `  `    ``// Keep track on parents of nodes ` `    ``// for front and backward search ` `    ``int` `s_parent[V], t_parent[V]; ` ` `  `    ``// queue for front and backward search ` `    ``list<``int``> s_queue, t_queue; ` ` `  `    ``int` `intersectNode = -1; ` ` `  `    ``// necessary initialization ` `    ``for``(``int` `i=0; i

Output:

```Path exist between 0 and 14
Intersection at: 7
*****Path*****
0 4 6 7 8 10 14
```

References

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