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What is the best alternative to Breadth-first search?
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A* Algorithm
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4
Experiences
Pros
3
Cons
1
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Pro
Complete
A* is complete, which means that it will always find a solution if it exists.
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Con
Not useful if you have many target nodes
If you have many target nodes and you don't know which one is closest to the main one, A* is not very optimal. This is because it needs to be run several times (once per target node) in order to get to all of them.
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Pro
Can be morphed into other algorithms
A* can be morphed into another path-finding algorithm by simply playing with the heuristics it uses and how it evaluates each node. This can be done to simulate Dijkstra, Best First Search, Breadth First Search and Depth First Search.
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Pro
Heuristic
A* expands on a node only if it seems promising. It's only focus is to reach the goal node as quickly as possible from the current node, not to try and reach every other node.
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Dijkstra's Algorithm
All
3
Experiences
Pros
2
Cons
1
Top
Pro
Uninformed algorithm
Dijkstra is an uninformed algorithm. This means that it does not need to know the target node beforehand. For this reason it's optimal in cases where you don't have any prior knowledge of the graph when you cannot estimate the distance between each node and the target.
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Con
Fails for negative edge weights
If we take for example 3 Nodes (A, B and C) where they form an undirected graph with edges: AB = 3, AC = 4, BC=-2, the optimal path from A to C costs 1 and the optimal path from A to B costs 2. If we apply Dijkstra's algorithm: starting from A it will first examine B because it is the closest node. and will assign a cost of 3 to it and therefore mark it closed which means that its cost will never be reevaluated. This means that Dijkstra's cannot evaluate negative edge weights.
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Pro
Good when you have multiple target nodes
Since Dijkstra picks edges with the smallest cost at each step it usually covers a large area of the graph. This is especially useful when you have multiple target nodes but you don't know which one is the closest.
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Built By the Slant team
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