Match3Algorithm is a C++ implementation of a Match-3 game logic system that efficiently detects and clears tile matches using graph-based techniques. The project focuses on tile matching, swapping mechanics, and efficient pattern recognition using depth-first search (DFS) and adjacency lists.
- Graph-Based Board Representation: Uses an adjacency list instead of a simple 2D grid for more flexibility.
- Depth-First Search (DFS) for Match Detection: Efficiently finds and clears matching tiles.
- Tile Swapping Mechanics: Allows valid swaps between adjacent tiles.
- Dynamic Board Updates: Handles cascading matches and tile refilling.
- Optimized Performance: Avoids unnecessary board scans, making it suitable for large-scale Match-3 implementations.
-
Board Initialization:
- The game board is represented as a graph, with each tile as a node and edges connecting adjacent tiles.
-
Tile Swapping:
- Players can swap adjacent tiles.
- The swap is validated to ensure it results in a match.
-
Match Detection:
- DFS is used to search for connected groups of matching tiles.
- Matching tiles are marked for removal.
-
Tile Removal & Refill:
- Matched tiles are cleared.
- New tiles drop down to fill empty spaces.
- The board is checked again for cascading matches.
Match3Algorithm/
│── src/ # Core implementation
│ ├── board.cpp # Board representation and management
│ ├── tile.cpp # Tile properties and interactions
│ ├── match.cpp # Match detection logic (DFS)
│── test/ # Unit tests
│── assets/ # Optional graphical assets (if applicable)
│── README.md # Project documentation
- C++11 or later
- CMake (for building)
git clone https://github.com/youssefmyh/Match3Algorithm.git
cd Match3Algorithm
mkdir build && cd build
cmake ..
make
./match3algorithm # Run the program
make test
Match-3 games are a classic genre, and this project provides a clean, efficient, and scalable approach to implementing their core mechanics. Whether you're a game developer, a student, or just curious about graph algorithms in game development, this project is a great resource.
Contributions are welcome! Feel free to fork the repo, open issues, or submit pull requests to enhance the algorithm and add new features.
This project is licensed under the MIT License.
For questions or suggestions, reach out via GitHub Issues.