An advanced algorithmic engine designed to visualize and benchmark search heuristics in a 2D grid environment. This Progressive Web App (PWA) evaluates the efficiency of informed and uninformed search strategies, providing a comparative analysis of path optimality and exploration density.
The engine provides a deep dive into several graph traversal strategies:
- Informed Search: A*, IDA* (Iterative Deepening A*), Greedy Best-First Search.
- Uninformed Search: Dijkstra’s (Uniform Cost), BFS, DFS, Iterative Deepening.
- Heuristics: Manhatten, Euclidean, and Chebyshev distance implementations for cost estimation.
- High-Performance Rendering: Optimized DOM manipulation and CSS transitions to maintain 60FPS during large-scale grid traversals (up to 2500+ nodes).
- Progressive Web App (PWA): Engineered with Service Workers for 100% offline functionality and rapid "Add to Home Screen" capability.
- State Management: Implemented a robust state machine to handle real-time algorithm interruptions, speed adjustments, and grid resets.
- Search Optimality: Compares the shortest path found by Dijkstra/A* against suboptimal Greedy approaches.
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Exploration Density: Visualizes the number of nodes visited ($O(V+E)$) to demonstrate the efficiency of
$h(n)$ heuristics. - Latency: Real-time visualization adjustments with sub-10ms logic updates.
- Frontend: Vanilla JavaScript (ES6+), HTML5, CSS3 (Neumorphic UI).
- Tooling: Vite (Build System), ESLint (Code Quality).
- Deployment: Vercel/PWA standards.
git clone https://github.com/Yeshwanth-kr/pathfinding-visualizer-pwa.gitnpm installnpm run dev
Author: Yeshwanth Krishna
Focus: Algorithms, Data Structures & Performance Optimization