| Feature | BFS | DFS |
|---|---|---|
| Data Structure | Queue | Stack / Recursion |
| Traversal | Level by level | Depth wise |
| Memory Usage | High | Low |
| Shortest Path | Yes | No |
| Completeness | Complete | Not always |
| Best Use Case | Shortest path | Deep exploration |
Introduction
BFS and DFS are the most basic algorithms used for traversing trees and graphs.
These algorithms are extremely helpful for most facets of the fields of Artificial Intelligence, Machine Learning, and Data Structures.
They systematically analyze the structures they are given.
These algorithms are very helpful for analyzing networks, and are essential for problems involving pathfinding and searching.
These algorithms are commonly included in exams for both undergraduate and competitive courses.
Definition
BFS examines a given graph and its neighboring nodes in layers, and in contrast, DFS explores a graph one level at a time and continues to further explore the neighboring nodes of the graph.
These two algorithms are unique in their strategies, memory usage, and applicable scenarios despite the fact that they are both utilized to traverse through graphs and trees.
Breadth First Search (BFS)
Working of BFS
- BFS starts from a source node.
- It examines and explores each of its immediate neighboring nodes.
- This algorithm uses a queue (FIFO) data structure.
- BFS explores graphs in layers.
- BFS in unweighted graphs guarantees finding the most efficient path.
Advantages of BFS
- BFS guarantees finding the most efficient path to the target destination.
- BFS is a complete and optimal algorithm.
- This algorithm is suitable for graphs that are shallow.
- BFS is easy to implement.
Disadvantages of BFS
- BFS, as an algorithm, consumes a lot of memory.
- This is not a suitable algorithm for graphs that are too deep.
- This algorithm is very slow when the branching factor is too large.
Depth First Search (DFS)
Working of DFS
- Like BFS, DFS also starts from a source node.
Because DFS exhaustively explores one branch, it can use a stack (LIFO) data structure, or it can also use recursion.
This algorithm also backtracks in the event a dead-end is reached.
DFS does not guarantee finding the most efficient path.
Advantages of DFS
- Low memory consumption
- Faster with deeper searches
- Easier to implement with recursion
- Can be applied to topological sort
Disadvantages of DFS
- Can get lost in infinite loops
- Non-optimal
- Does not guarantee a shortest path
Feature BFS DFS
- Data Structure Queue Stack / Recursion
- Traversal Level by level Depth wise
- Memory More Less
- Shortest Path Yes No
- Completeness Complete Not always
- Best Use Case Shortest Path Deep exploration
- Applications of BFS and DFS
Applications of BFS
- Algorithms to find the shortest path
- Web crawling
- Navigation using GPS
- Broadcasting networks
- Artificial Intelligence problem solving
Applications of DFS
- Cycle in a graph detection
- Topological sorting
- Maze solving
- Games with puzzles
- AI decision trees
Frequent Questions (FAQ)
What is the difference of BFS and DFS?
BFS uses a queue to traverse the nodes level by level, while DFS uses a stack or does recursion to traverse all the way to the bottom first.
Which algorithm is more memory consuming?
BFS is more memory consuming than DFS because it keeps all the nodes that are adjacent at the same level.
Is it the case that BFS is better than DFS?
It is a case by case basis. BFS is better with problems that require finding the shortest path while DFS is better with problems that require going to great depths.
Which algorithm applies to AI?
- Both AI state-space search, planning, and problem-solving, use BFS and DFS.
Which is quicker, BFS or DFS?
DFS is generally quicker with deep graphs, while BFS is quicker with shallow graphs.
