About This Project
An interactive visualization tool for understanding pathfinding algorithms
What is Pathfinding Visualizer?
Pathfinding Visualizer is an interactive web application that helps you understand how different pathfinding algorithms work. By visualizing the step-by-step process of finding the shortest path between two points, you can gain a deeper understanding of these fundamental computer science algorithms.
Whether you're a student learning about graph algorithms, a developer brushing up on your fundamentals, or simply curious about how GPS navigation and game AI work under the hood, this tool provides an intuitive way to explore these concepts.
Implemented Algorithms
Breadth-First Search (BFS)
Explores all neighbors at the current depth before moving to nodes at the next depth level. Guarantees the shortest path in unweighted graphs.
Depth-First Search (DFS)
Explores as far as possible along each branch before backtracking. Does not guarantee the shortest path but uses less memory than BFS.
Dijkstra's Algorithm
The classic shortest path algorithm that works with weighted graphs. Always finds the optimal path by exploring nodes in order of their distance from the start.
A* Search
An informed search algorithm that uses heuristics (Manhattan distance) to guide the search toward the goal. Often faster than Dijkstra's while still guaranteeing the shortest path.
Wall Follower
A classic maze-solving algorithm using the right-hand rule. Keeps one hand on the wall and follows it until reaching the goal. Works well for simply-connected mazes.
Bidirectional BFS
Searches from both the start and end nodes simultaneously, meeting in the middle. Can be significantly faster for long paths as it reduces the search space exponentially.
Swarm Algorithm
A weighted hybrid of A* and Greedy Best-First that creates interesting swarm-like expansion patterns. Balances between exploration and exploitation for visually appealing results.
Theta*
An any-angle pathfinding algorithm that produces shorter, more realistic paths by allowing movement in any direction, not just along grid edges. Uses line-of-sight checks.
Bellman-Ford
A versatile shortest path algorithm that can handle negative edge weights (unlike Dijkstra). Slower but useful for graphs with varying edge costs.
IDA* (Iterative Deepening A*)
Combines the space efficiency of depth-first search with the optimality of A*. Uses iterative deepening with a heuristic threshold that increases each iteration.
Best-First Search
A pure heuristic search that always expands the most promising node based on estimated distance to goal. Fast but may not find the shortest path.
Flood Fill
A wavefront expansion algorithm that floods outward from the start in all directions simultaneously, like water filling a container. Guarantees shortest path.
How to Use
- 1Place the Start Node: Click on any cell to place the green start node.
- 2Place the End Node: Click on another cell to place the red end node.
- 3Draw Walls: Click and drag to create walls that the algorithm must navigate around.
- 4Select an Algorithm: Choose from BFS, DFS, Dijkstra's, or A* from the dropdown.
- 5Visualize: Click the Visualize button to watch the algorithm find the path!
Tech Stack
About the Author
Hi! I'm Abdellah Anca, the creator of this project. I built this pathfinding visualizer to help others understand these fascinating algorithms in an interactive and visual way.
This project demonstrates my passion for algorithms, data structures, and creating educational tools that make complex concepts accessible to everyone.