A* Optimization
A functional and optimized implementation of the A* algorithm
Pathfinding Optimization
Primary Programmer
February 2025 – March 2025
A from-scratch implementation of the A* pathfinding algorithm optimized for performance on grid-based maps. Includes analysis of heuristic selection, open/closed list efficiency, and memory usage — with profiling to demonstrate improvements over naive approaches.
Software Engineering Takeaways
In addition to the typical challenges posed by implementing an A-Star algorithm, I precalculated neighbors and stored the resulting data in a single byte. This was done by using the bits of an unsigned int to represent the 8 possible neighbors of tiles at the change of every map.
Software Engineering Takeaways
This project was directly related to enemy intelligence as basic search and pathfinding algorithms are essential components of enemy intelligence. This was refined by taking map dimension and wall configurations into account so that the path generated would be one walkable by the agent.
Project Contribution Takeaways
This project was directly related to enemy intelligence as basic search and pathfinding algorithms are essential components of enemy intelligence. This was refined by taking map dimension and wall configurations into account so that the path generated would be one walkable by the agent.
Smooth Rubberbanded Path Generation
Performance Based Optimization
