How does iterative deepening search combine advantages of depth-first and breadth-first search?

Iterative deepening search effectively combines the strengths of depth-first and breadth-first search by employing a depth limit that increases with each iteration. This method allows the algorithm to explore nodes to a specific depth in a depth-first fashion before increasing that limit and starting over.

The primary advantage of this approach is that it retains the memory efficiency of depth-first search, which only requires storing the current path and a small number of additional nodes, while also ensuring that all nodes at a shallower depth are examined before deeper levels are considered—similar to breadth-first search. This systematic depth limitation enables the algorithm to find the shallowest solution without exhausting memory resources, a common issue in breadth-first search.

Safer memory usage and the ability to find solutions at the shallowest level make iterative deepening search a valuable strategy, particularly in environments where the depth of the solution is not known in advance.