Let’s consider a simple grid, where any point is connected with at most 4 other points (North-East-West-South neighborhood).
I have to write program, that computes minimal route from selected initial point to any of goal points, which are connected (there is route consisting of goal points between any two goals). Of course there can be obstacles on grid.
My solution is quite simple: I’m using A* algorithm with variable heuristic function h(x) – manhattan distance from x to nearest goal point. To find nearest goal point I have to do linear search (in O(n), where n – number of goal points). Here is my question: is there any more efficient solution (heuristic function) to dynamically find nearest goal point (where time < O(n))?
Or maybe A* is not good way to solve that problem?
How many goals, tens or thousands? If tens your way will work fine, if thousands then nearest neighbor search will give you ideas on setting up your data to search quickly.
The tradeoffs are obvious, spatially organizing your data to search will take time and on small sets brute force will be simpler to maintain. Since you’re constantly evaluating I think that structuring the data will be worthwhile at very low numbers of points.
An alternate way to do this would be a modified flood fill algorithm that stops once it reaches a destination point during the flood.