当树的深度可以变化时，宽度优先搜索通常是最好的方法，并且您只需要搜索树的一部分来寻找解决方案。例如，寻找从起始值到最终值的最短路径是使用BFS的好地方。

深度优先搜索通常用于需要搜索整个树的情况。它比BFS更容易实现(使用递归)，并且需要更少的状态:BFS需要存储整个“边界”，DFS只需要存储当前元素的父节点列表。

For BFS, we can consider Facebook example. We receive suggestion to add friends from the FB profile from other other friends profile. Suppose A->B, while B->E and B->F, so A will get suggestion for E And F. They must be using BFS to read till second level. DFS is more based on scenarios where we want to forecast something based on data we have from source to destination. As mentioned already about chess or sudoku. Once thing I have different here is, I believe DFS should be used for shortest path because DFS will cover the whole path first then we can decide the best. But as BFS will use greedy's approach so might be it looks like its the shortest path, but the final result might differ. Let me know whether my understanding is wrong.

当树的深度可以变化时，宽度优先搜索通常是最好的方法，并且您只需要搜索树的一部分来寻找解决方案。例如，寻找从起始值到最终值的最短路径是使用BFS的好地方。

深度优先搜索通常用于需要搜索整个树的情况。它比BFS更容易实现(使用递归)，并且需要更少的状态:BFS需要存储整个“边界”，DFS只需要存储当前元素的父节点列表。

来自 http://www.programmerinterview.com/index.php/data-structures/dfs-vs-bfs/

一个BFS的例子

这里有一个BFS的例子。这类似于级别顺序树遍历，其中我们将使用迭代方法的队列(大多数递归将最终使用DFS)。数字表示BFS中节点被访问的顺序:

在深度优先搜索中，从根开始，尽可能地跟随树的一个分支，直到找到要查找的节点或找到叶节点(没有子节点)。如果您选中了一个叶节点，那么您将继续在最近的具有未探索的子节点的父节点上搜索。

DFS的一个例子

下面是一个DFS的示例。我认为二叉树中的后序遍历将首先从叶层开始工作。数字表示DFS中节点被访问的顺序:

DFS和BFS的区别

比较BFS和DFS, DFS的最大优势是它的内存需求比BFS低得多，因为它不需要在每一层存储所有的子指针。根据数据和您正在寻找的内容，DFS或BFS都可能是有利的。

For example, given a family tree if one were looking for someone on the tree who’s still alive, then it would be safe to assume that person would be on the bottom of the tree. This means that a BFS would take a very long time to reach that last level. A DFS, however, would find the goal faster. But, if one were looking for a family member who died a very long time ago, then that person would be closer to the top of the tree. Then, a BFS would usually be faster than a DFS. So, the advantages of either vary depending on the data and what you’re looking for.

另一个例子是Facebook;关于朋友的朋友的建议。我们需要直接的朋友建议我们在哪里可以使用BFS。可能是寻找最短路径或检测周期(使用递归)，我们可以使用DFS。

这在很大程度上取决于搜索树的结构以及解的数量和位置(也就是搜索项)。

If you know a solution is not far from the root of the tree, a breadth first search (BFS) might be better. If the tree is very deep and solutions are rare, depth first search (DFS) might take an extremely long time, but BFS could be faster. If the tree is very wide, a BFS might need too much memory, so it might be completely impractical. If solutions are frequent but located deep in the tree, BFS could be impractical. If the search tree is very deep you will need to restrict the search depth for depth first search (DFS), anyway (for example with iterative deepening).

但这些只是经验法则;你可能需要尝试一下。

我认为在实践中，你通常不会以纯粹的形式使用这些算法。可能会有一些启发式方法，有助于首先探索搜索空间中有希望的部分，或者您可能希望修改搜索算法，以便能够有效地并行化它。

我认为这取决于你所面临的问题。

简单图上的最短路径-> BFS 所有可能的结果-> DFS 在图上搜索(将树，martix也视为图)-> DFS ．...