Route-Optimization

This repository implements Dijkstra's and A algorithms* for route optimization, with visualization using the Pygame library. The project allows users to interactively explore the shortest path between two points on a grid.

Table of Contents

• Overview
• Features
• Technologies
• Setup
• Usage
• Algorithm Details
• Contributing
• License

Overview

Route optimization is essential in many applications like GPS navigation, logistics, and AI for games. This project visualizes how Dijkstra's and A* algorithms can be used to find the shortest path between two points on a grid, taking into account various obstacles and terrain types.

Features

• Dijkstra's Algorithm: Guarantees the shortest path, works for all edge weights.
• A Algorithm*: More efficient with heuristics (such as Manhattan Distance), designed for grid-based pathfinding.
• Grid Visualization: See how each algorithm explores the grid, one node at a time.
• Interactive Interface: Users can create obstacles, set start and end points, and visualize the pathfinding process.
• Comparison Mode: Compare how Dijkstra and A* perform on the same problem.

Technologies

• Python 3.x
• Pygame Library

Setup

1. Clone the Repository:

   git clone https://github.com/yourusername/route-optimization.git
   cd route-optimization

2. Install Dependencies: You need to have Python installed. To install the required libraries, run:

   pip install -r requirements.txt

3. Run the Project:

   python dijistra.py

   python AStar.py

Usage

1. Grid Setup:

   • Left-click to place the start point.
   • Right-click to place the end point.
   • Drag to place obstacles on the grid.

2. Start the Visualization:

   • Press D for Dijkstra's Algorithm.
   • Press A for A* Algorithm.

3. Reset Grid:

   • Press R to reset the grid and start over.

Algorithm Details

• Dijkstra's Algorithm: Explores every possible path uniformly, making it slower but guaranteed to find the shortest path even with varying edge costs.
• A * Algorithm: Uses a heuristic (such as the Manhattan Distance or Euclidean Distance) to guide the search, which makes it faster in practice for many grid-based problems.

Contributing

Contributions are welcome! If you find any issues or have suggestions for improvements, feel free to open an issue or submit a pull request.

License

This project is licensed under the MIT License - see the LICENSE file for details.