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Home/ Questions/Q 6190751
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Editorial Team
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Editorial Team
Asked: May 24, 20262026-05-24T02:38:40+00:00 2026-05-24T02:38:40+00:00

Firstly , For those of your who dont know – Anytime Algorithm is an

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Firstly , For those of your who dont know – Anytime Algorithm is an algorithm that get as input the amount of time it can run and it should give the best solution it can on that time.

Weighted A* is the same as A* with one diffrence in the f function :

(where g is the path cost upto node , and h is the heuristic to the end of path until reaching a goal)

Original = f(node) = g(node) + h(node)

Weighted = f(node) = (1-w)g(node) +h(node)

My anytime algorithm runs Weighted A* with decaring weight from 1 to 0.5 until it reaches the time limit.

My problem is that most of the time , it takes alot time until this it reaches a solution , and if given somthing like 10 seconds it usaully doesnt find solution while other algorithms like anytime beam finds one in 0.0001 seconds.

Any ideas what to do?

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  1. Editorial Team
    Editorial Team
    2026-05-24T02:38:41+00:00Added an answer on May 24, 2026 at 2:38 am

    If I were you I’d throw the unbounded heuristic away. Admissible heuristics are much better in that given a weight value for a solution you’ve found, you can say that it is at most 1/weight times the length of an optimal solution.

    A big problem when implementing A* derivatives is the data structures. When I implemented a bidirectional search, just changing from array lists to a combination of hash augmented priority queues and array lists on demand, cut the runtime cost by three orders of magnitude – literally.

    The main problem is that most of the papers only give pseudo-code for the algorithm using set logic – it’s up to you to actually figure out how to represent the sets in your code. Don’t be afraid of using multiple ADTs for a single list, i.e. your open list. I’m not 100% sure on Anytime Weighted A*, I’ve done other derivatives such as Anytime Dynamic A* and Anytime Repairing A*, not AWA* though.

    Another issue is when you set the g-value too low, sometimes it can take far longer to find any solution that it would if it were a higher g-value. A common pitfall is forgetting to check your closed list for duplicate states, thus ending up in a (infinite if your g-value gets reduced to 0) loop. I’d try starting with something reasonably higher than 0 if you’re getting quick results with a beam search.

    Some pseudo-code would likely help here! Anyhow these are just my thoughts on the matter, you may have solved it already – if so good on you 🙂

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