How do iterative deepening search and breadth-first search compare?

How do iterative deepening search and breadth-first search compare?

Iterative deepening search (IDS) combines the benefits of depth-first search and breadth-first search. It explores the search space in a depth-first manner but does so iteratively, increasing the depth limit with each iteration. This allows it to find the shortest path like breadth-first search while using less memory.

What are the advantages of using iterative deepening search?

The main advantages of IDS are its low memory usage compared to breadth-first search and its ability to find the optimal solution like breadth-first search. It also avoids the pitfalls of depth-first search, such as getting stuck in deep branches.

Are there any disadvantages to iterative deepening search?

Yes, one disadvantage is that IDS can be slower than breadth-first search because it repeatedly explores the same nodes in each iteration. This can lead to increased computation time, especially in large search spaces.

In what scenarios would you recommend using breadth-first search instead?

Breadth-first search is recommended when the search space is shallow or when you need to find the shortest path in an unweighted graph, as it guarantees the shortest solution.