Disclaimer: I really believe that this is not a duplicate of similar questions. I've read those, and they (mostly) recommend using a heap or a priority queue. My question is more of the "I don't understand how those would work in this case" kind.
In short:
I'm referring to the typical A* (A-star) pathfinding algorithm, as described (for example) on Wikipedia:
https://en.wikipedia.org/wiki/A*_search_algorithm
More specifically, I'm wondering about what's the best data structure (which can be a single well known data structure, or a combination of those) to use so that you never have O(n) performance on any of the operations that the algorithm requires to do on the open list.
As far as I understand (mostly from the Wikipedia article), the operations needed to be done on the open list are as follows:
The elements in this list need to be Node instances with the following properties:
- position (or coordinates). For the sake of argument, let's say this is a positive integer ranging in value from 0 to 64516 (I'm limiting my A* area size to 254x254, which means that any set of coordinates can be bit-encoded on 16 bits)
- F score. This is positive floating point value.
Given these, the operations are:
- Add a node to the open list: if a node with the same position (coordinates) exists (but, potentially, with a different F score), replace it.
- Retrieve (and remove) from the open list the node with the lowest F score
- (Check if exists and) retrieve from the list a node for a given position (coordinates)
As far as I can see, the problem with using a Heap or Priority Queue for the open list are:
- These data structure will use the F-score as sorting criteria
- As such, adding a node to this kind of data structure is problematic: how do you check optimally that a node with a similar set of coordinates (but a different F Score) doesn't already exist. Furthermore, even if you somehow are able to do this check, if you actually find such a node, but it is not on the top of the Heap/Queue, how to you optimally remove it such that the Heap/Queue keeps its correct order
- Also, checking for existence and removing a node based on its position is not optimal or even possible: if we use a Priority Queue, we have to check every node in it, and remove the corresponding one if found. For a heap, if such a removal is necessary, I imagine that all remaining elements need to be extracted and re-inserted, so that the heap still remains a heap.
- The only remaining operating where such a data structure would be good is when we want to remove the node with the lowest F-score. In this case the operation would be O(Log(n)).
Also, if we make a custom data structure, such as one that uses a Hashtable (with position as key) and a Priority Queue, we would still have some operations that require suboptimal processing on either of these: In order to keep them in sync (both should have the same nodes in them), for a given operation, that operation will always be subomtimal on one of the data structures: adding or removing a node by position would be fast on the Hashtable but slow on the Priority Queue. Removing the node with the lowest F score would be fast on the Priority Queue but slow on the Hashtable.
What I've done is make a custom Hashtable for the nodes that uses their position as key, that also keeps track of the current node with the lowest F score. When adding a new node, it checks if its F score is lower than the currently stored lowest F score node, and if so, it replaces it. The problem with this data structure comes when you want to remove a node (whether by position or the lowest F scored one). In this case, in order to update the field holding the current lowest F score node, I need to iterate through all the remaining node in order to find which one has the lowest F score now.
So my question is: is there a better way to store these ?
