Efficient Pathfinding Algorithms for Large-Scale Graphs

Problem statement

Pathfinding is a core primitive in routing, logistics, and distributed systems. As graph sizes grow, naive traversal approaches become computationally expensive and impractical for real-time use cases.

Approach

This project analyzed and implemented multiple classical and optimized pathfinding algorithms, focusing on scalability and performance:

• Implemented and compared Dijkstra, A*, and heuristic-based search algorithms
• Analyzed time and space complexity under different graph densities
• Evaluated performance tradeoffs between preprocessing cost and query latency
• Studied heuristic admissibility and its effect on optimality guarantees

Results

• Heuristic-guided search (A*) significantly reduced explored state space compared to Dijkstra
• Proper heuristic design improved performance without sacrificing correctness
• Memory usage became a primary bottleneck at scale, influencing algorithm choice

Practical relevance

These techniques directly apply to:

• Routing and navigation systems
• Distributed scheduling and resource allocation
• Backend services requiring low-latency graph traversal

Lessons learned

Algorithmic correctness alone is insufficient at scale. Practical systems require careful balancing of computation, memory, and latency constraints.