Breadth First Search

1. Breadth
First Search
PREPARED BY : SUPRIYO DANA
UNIVERSITY ROLL NO : 34200123067
PAPER NAME : ARTIFICIAL INTELLIGENCE; PAPER CODE: PECIT501B
DEPARTMENT OF COMPUTER SCIENCE & ENGINEERING
FUTURE INSTITUTE OF TECHNOLOGY

2. CONTENT
Introduction to BFS
Key Characteristics
Use Cases
How BFS Works
Example
Advantages and Disadvantages
Conclusion and Applications
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3. o Breadth-First Search (BFS) is a graph
traversal algorithm that explores vertices
level by level. It begins at a starting node
and explores all its neighboring nodes at
the present depth prior to moving on to
nodes at the next depth level.
Introduction to BFS
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4. Key Characteristics & Use Cases
Key Characteristics:
• BFS uses a queue data structure.
• It explores nodes in layers, i.e., all nodes at the present depth are visited
before any nodes at the next depth level.
Use Cases :
• Finding the shortest path in unweighted graphs.
• Crawling the web.
• Peer-to-peer networks.
• Network broadcast.
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5. How BFS Works
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Step-by-Step Process:
• Initialization: Start from the root node, mark it as visited, and enqueue it.
• Dequeue & Explore: Dequeue a node from the front of the queue, and explore
its unvisited neighbors.
• Enqueue Neighbors: Mark each unvisited neighbor as visited and enqueue
them.
• Repeat: Repeat the process until the queue is empty.
Queue Data Structure:
• A queue is essential to manage the nodes to be explored, following the First-In-
First-Out (FIFO) principle.
Level-wise Exploration:
• BFS ensures that nodes are explored in the order of their distance from the
root, level by level.

6. Example
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7. Example
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8. Example
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9. Advantages and Disadvantages
oShortest Path: Guarantees finding the shortest
path in unweighted graphs.
oSimple: Easy to implement and understand.
Advantages
oMemory-Intensive: Can be memory-intensive for
large graphs or wide trees.
oNot Suitable for Weighted Graphs: BFS does not
handle weighted edges well; Dijkstra's algorithm or
A* is preferred in those cases.
Disadvantages
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10. Conclusion and Applications
BFS is a foundational graph traversal technique that explores nodes level by level. It is
effective for unweighted graphs and certain tree-based problems.
BFS is widely used in AI, networking, and other fields where graph-based problem-
solving is required. Examples include finding shortest paths, solving puzzles, and
searching in peer-to-peer networks.
BFS is a versatile algorithm that, despite its simplicity, has profound applications
across many domains.
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11. THANK YOU
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