What is the BFS Algorithm: A Simple Guide for Beginners

Have you ever wondered how your GPS determines the shortest routes or how social media suggests friends you may know? These everyday tools typically employ a technique known as the BFS algorithm.

BFS full form is breadth-first search. It is a fundamental yet powerful tool, as it is used in data structures and artificial intelligence to search through data at a specific level at a time.

As the digital and tech-savvy world continues to grow, the demand for intelligent algorithms is also increasing. The AI market is also expected to reach USD 1236.47 billion by 2030, with a CAGR of 32.9% from 2025 to 2030. This growth in the market makes it essential to understand the tools that power it, such as BFS.

Let's explore the BFS algorithm in this blog, how it works, and why it is essential.

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What is the BFS algorithm?

The BFS algorithm is a method that helps you explore data, such as graphs. This process begins with a point known as the root or source node. It explores all the nodes connected to it or nearby, examining each point individually to ensure that nothing is left unattended or overlooked.

This algorithm doesn’t work like other methods; it moves horizontally by checking all the points before moving deeper. It also uses a queue to keep track of all the nodes to visit next.

BFS is commonly used to:

• Find the smallest/shortest path in networks or maps.
• Check if something exists within the structure.
• Explore all the connected parts of the data step by step.

This makes it popular in various fields, such as artificial intelligence and data structures.

What is the BFS Algorithm in Data Structures?

In data structures, the BFS algorithm is used for traversing graphs and trees. It systematically and properly explores all nodes in the vicinity at the current depth before moving to a deeper level. This system makes it ideal for problems where the shortest or level-order traversal is required.

How does BFS Work in Data Structures?

A simple example of understanding BFS in data structures is navigation systems like Google Maps, which find the shortest path between two locations.

It works in the following ways:

• Using a queue to track the order of node visits.
• Mark each node as visited to ensure there is no repetition.
• Visiting the neighbours of each node before conducting deeper searches.

Also Read:- How to Choose the Best Mini-Projects in Online BCA for Skill Development?

BFS Algorithm in Artificial Intelligence (AI)

In AI, the BFS algorithm plays a significant role in helping you find paths and solve problems. It is typically used in search-based AI models, where an agent explores a set of possible actions to achieve a specific goal.

How does BFS work in AI?

A simple example of understanding BFS in AI is games like solving puzzles or making chess moves. This is how this system works:

• Treating each possible state, step, or action as a node in the search tree.
• Exploring the closest unvisited node first ensures that the shortest path to the goal is found.

Advantages and Limitations of the BFS Algorithm

The BFS algorithm is often used in programming, AI, data analysis, etc. It is beneficial when you need to find the shortest or closest path. But like any other method or algorithm, it also has its advantages and limitations, such as:

1. Advantages:

• It finds the shortest path in an unweighted graph from the starting point to the target point, ensuring that it requires a limited number of steps to reach.
• It uses a queue and follows a simple level-by-level approach, making it easy to understand and beginner-friendly.
• Reliable for providing complete solutions.
• It checks all the nearby possibilities first before conducting further research, which helps when navigating maps or solving puzzles.

2. Limitations:

• It consumes a significant amount of memory in storing all the nodes at each level, particularly in wide graphs.
• It doesn’t work when all the different paths have different costs.
• BFS takes a longer time to explore if the target node is deep down, as it explores all the upper levels first.
• It may also take unnecessary paths before reaching the solution, which can be ineffective in specific problems.

Why Should You Learn the BFS at the Beginner Level?

Learning BFS algorithms early can help you build a foundation of computer science and problem-solving. Here are the key reasons why you should start learning this algorithm at a beginner's level:

• BFS builds logical thinking by teaching you to solve problems step by step, a skill essential in both programming and real-life coding challenges.
• From social media to GPS, it is being used everywhere in daily and real-world applications.
• BFS problems are common in coding tests and job interviews, which also makes you feel more confident and comfortable.
• It's straightforward to understand, making it ideal for you to learn algorithm logic quickly.
• It is relevant for technical careers, as it forms the backbone of solutions in web crawlers, network routing, AI search engines, and recommendation systems.

Learn Algorithms with Amity University Online

Learning algorithms can be easier and more effective if you choose the right platform. Amity University Online is India's first university to offer industry-relevant programmes online. These programmes are designed in a way that they build a strong foundation for you in the field of computer science and programming.

Here are a few benefits of choosing Amity Online for your learning journey:

• You have the opportunity to learn from experienced faculty who have real-world experience in scenario-based learning.
• The programmes are 100% online, allowing you to study at your own pace with flexible schedules.
• You get the physical books delivered to your doorstep at your convenience.
• The courses offered by Amity are focused on learning and updated with the latest technological trends, while including first-hand experiences for you.
• Amity offers flexible and affordable EMI options, which help you fulfil your dreams without worrying about finances.

Conclusion

Understanding the breadth-first search algorithm fosters critical thinking and strengthens foundational problem-solving skills. It's widely used in real-world tools, such as puzzle solvers and robotic navigation.

Moreover, learning BFS offers you a clear and practical entry point into algorithmic thinking. With the support of a reputable platform like Amity University Online, you can master core computing concepts flexibly—anytime, anywhere, at your own pace.