Iterative deepening A*

Iterative deepening A* (IDA*) is a graph traversal and path search algorithm that can find the shortest path between a designated start node and any member of a set of goal nodes in a weighted graph. It is a variant of iterative deepening depth-first search that borrows the idea to use a heuristic function to conservatively estimate the remaining cost to get to the goal from the A* search algorithm. Since it is a depth-first search algorithm, its memory usage is lower than in A*, but unlike ordinary iterative deepening search, it concentrates on exploring the most promising nodes and thus does not go to the same depth everywhere in the search tree. Unlike A*, IDA* does not utilize dynamic programming and therefore often ends up exploring the same nodes many times. While the standard iterative deepening depth-first search uses search depth as the cutoff for each iteration, the IDA* uses the more informative , where is the cost to travel from the root to node and is a problem-specific heuristic estimate of the cost to travel from to the goal. The algorithm was first described by Richard Korf in 1985. Iterative-deepening-A* works as follows: at each iteration, perform a depth-first search, cutting off a branch when its total cost exceeds a given threshold. This threshold starts at the estimate of the cost at the initial state, and increases for each iteration of the algorithm. At each iteration, the threshold used for the next iteration is the minimum cost of all values that exceeded the current threshold. As in A*, the heuristic has to have particular properties to guarantee optimality (shortest paths). See Properties below. path current search path (acts like a stack) node current node (last node in current path) g the cost to reach current node f estimated cost of the cheapest path (root..node..goal) h(node) estimated cost of the cheapest path (node..