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Best First Search (Informed Search)

https://www.geeksforgeeks.org/best-first-search-informed-search/

最好优先搜索

BFS DFS使用暴力方式盲目搜索。

Best First 方式，使用评价函数来决定最有希望的邻居节点，然后做节点扩展。

实现方式，评价函数中考虑 启发式规则， 此规则利用的Informed信息。

In BFS and DFS, when we are at a node, we can consider any of the adjacent as the next node. So both BFS and DFS blindly explore paths without considering any cost function. 

The idea of Best First Search is to use an evaluation function to decide which adjacent is most promising and then explore.

Best First Search falls under the category of Heuristic Search or Informed Search.

Implementation of Best First Search:

使用优先队列或者堆来存储节点的花费

BFS使用queue

DFS使用stack

此方法使用 优先队列

We use a priority queue or heap to store the costs of nodes that have the lowest evaluation function value. So the implementation is a variation of BFS, we just need to change Queue to PriorityQueue. 

// Pseudocode for Best First Search
Best-First-Search(Graph g, Node start)
    1) Create an empty PriorityQueue
       PriorityQueue pq;
    2) Insert "start" in pq.
       pq.insert(start)
    3) Until PriorityQueue is empty
          u = PriorityQueue.DeleteMin
          If u is the goal
             Exit
          Else
             Foreach neighbor v of u
                If v "Unvisited"
                    Mark v "Visited"                    
                    pq.insert(v)
             Mark u "Examined"                    
End procedure

算法复杂度中

n -- 对最坏情况下需要访问n元素，

log n -- 使用堆或者优先队列，获取最值得复杂度。

Analysis : 

• The worst-case time complexity for Best First Search is O(n * log n) where n is the number of nodes. In the worst case, we may have to visit all nodes before we reach goal. Note that priority queue is implemented using Min(or Max) Heap, and insert and remove operations take O(log n) time.
• The performance of the algorithm depends on how well the cost or evaluation function is designed.

特殊的情况有 贪婪优先搜索 和 A星搜索

Special cases of Best first search:

1. Greedy Best first search algorithm
2. A* search algorithm

Sample Code

此代码中，仅仅考虑扩展时候的最小代价。

// C++ program to implement Best First Search using priority
// queue
#include <bits/stdc++.h>
using namespace std;
typedef pair<int, int> pi;

vector<vector<pi> > graph;

// Function for adding edges to graph
void addedge(int x, int y, int cost)
{
    graph[x].push_back(make_pair(cost, y));
    graph[y].push_back(make_pair(cost, x));
}

// Function For Implementing Best First Search
// Gives output path having lowest cost
void best_first_search(int actual_Src, int target, int n)
{
    vector<bool> visited(n, false);
    // MIN HEAP priority queue
    priority_queue<pi, vector<pi>, greater<pi> > pq;
    // sorting in pq gets done by first value of pair
    pq.push(make_pair(0, actual_Src));
    int s = actual_Src;
    visited[s] = true;
    while (!pq.empty()) {
        int x = pq.top().second;
        // Displaying the path having lowest cost
        cout << x << " ";
        pq.pop();
        if (x == target)
            break;

        for (int i = 0; i < graph[x].size(); i++) {
            if (!visited[graph[x][i].second]) {
                visited[graph[x][i].second] = true;
                pq.push(make_pair(graph[x][i].first,graph[x][i].second));
            }
        }
    }
}

// Driver code to test above methods
int main()
{
    // No. of Nodes
    int v = 14;
    graph.resize(v);

    // The nodes shown in above example(by alphabets) are
    // implemented using integers addedge(x,y,cost);
    addedge(0, 1, 3);
    addedge(0, 2, 6);
    addedge(0, 3, 5);
    addedge(1, 4, 9);
    addedge(1, 5, 8);
    addedge(2, 6, 12);
    addedge(2, 7, 14);
    addedge(3, 8, 7);
    addedge(8, 9, 5);
    addedge(8, 10, 6);
    addedge(9, 11, 1);
    addedge(9, 12, 10);
    addedge(9, 13, 2);

    int source = 0;
    int target = 9;

    // Function call
    best_first_search(source, target, v);

    return 0;
}

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