What are the complexities of different pathfinding algorithms?

What are the complexities of different pathfinding algorithms?

Pathfinding algorithms like A*, Dijkstra's, and BFS have different complexities. A* has a complexity of O(b^d), Dijkstra's is O(V^2) or O(E + V log V) with a priority queue, and BFS is O(V + E). Would you like to know more about a specific algorithm?

Can you explain why A* is often preferred over Dijkstra's?

A* is often preferred because it uses heuristics to guide its search, making it faster in many cases. Dijkstra's algorithm explores all possible paths, which can be slower, especially in large graphs. A* balances between exploring paths and using heuristics to find the shortest path more efficiently.

What types of heuristics are commonly used in A*?

Common heuristics for A* include the Manhattan distance, Euclidean distance, and Chebyshev distance, depending on the grid or graph structure. These heuristics help estimate the cost to reach the goal from a given node.

Are there any scenarios where Dijkstra's is more suitable?

Yes, Dijkstra's is more suitable in scenarios where all edge weights are non-negative, and you need the absolute shortest path without heuristics. It's also useful in dense graphs where the overhead of maintaining a priority queue in A* may not provide significant benefits.