Have you ever wondered how GPS finds the shortest route to your destination? Or how game characters navigate through complex mazes? The secret lies in pathfinding algorithms! In this blog, I’ll take you through my journey of building a Pathfinding Visualizer—a tool that brings these algorithms to life with colorful visuals and interactive grids. It’s not just about code; it’s about watching algorithms ‘think’ and solve puzzles in real-time. Think of it as a digital maze solver on steroids!

What is a Pathfinding Visualizer?

A Pathfinding Visualizer is a tool that demonstrates how algorithms like BFS, DFS, A*, Dijkstra, and Greedy Best-First Search find the shortest path between two points on a grid. It’s a great way to understand how these algorithms work under the hood, and it’s also super satisfying to watch!

Photo Suggestion: Add a screenshot of the main interface of your visualizer. Highlight the grid, start/end points, and walls.

The Pathfinding Visualizer in action—ready to solve some mazes! [green=start; red =end]

The Algorithms Behind the Magic

Let’s dive into the algorithms that power the visualizer. Each algorithm has its own unique way of solving the maze, and watching them work is like seeing different personalities in action!

1. Breadth-First Search (BFS)

BFS explores all neighboring nodes at the present depth before moving on to nodes at the next depth level. It’s like ripples spreading out in a pond—calm, methodical, and guaranteed to find the shortest path in an unweighted graph.

Math Alert:

BFS uses a queue to keep track of nodes to visit. The time complexity is O(V + E), where V is the number of vertices (nodes) and E is the number of edges (connections).

ASCII Diagram:

Start (S)
|
1 --- 2 --- 3
|     |     |
4 --- 5 --- 6
      |     |
      7 --- Goal (G)

• •BFS explores level by level: S → 1 → 2 → 3 → 4 → 5 → 6 → 7 → G.

BFS exploring the grid—like ripples spreading out!