Discover published sets by community

Although not a typical shortest path algorithm, shotgun hill climbing is a heuristic search algorithm that can be applied to find paths. It is a variant of hill climbing that tries multiple random initial states and chooses the best one.

Greedy best-first search is a search algorithm that expands the node that appears to be closest to the goal, as determined by a heuristic function. However, it doesn't always find the shortest path because it prioritizes proximity to the goal over path cost.

Dijkstra's algorithm finds the shortest path from a single source to all other vertices in a weighted graph with non-negative edge weights. It's a greedy algorithm that iteratively selects the vertex with the minimum distance from the source, updating the distances of its adjacent vertices.

Bidirectional search is a graph search algorithm that finds the shortest path from an initial vertex to a goal vertex by running two simultaneous searches—one forward from the initial vertex, and one backward from the goal vertex—and stopping when the two searches meet.

Breadth-first search is an algorithm for traversing or searching tree or graph data structures. In unweighted graphs, BFS can be used to find the shortest path from a single source vertex to all other vertices by exploring neighbors level by level.

A* search algorithm is used for finding the shortest path to a goal node in a weighted graph using heuristics to guide its search. It combines Dijkstra's algorithm with a best-first search.

The Floyd-Warshall algorithm finds the shortest paths between every pair of vertices in a weighted graph. This algorithm works for both positive and negative edge weights, but cannot handle negative cycles.

Johnson's algorithm finds shortest paths between all pairs of vertices in a sparse weighted directed graph. It combines both Dijkstra's and Bellman-Ford algorithms by reweighting edges to enable non-negative weights, and then using Dijkstra's algorithm for every vertex.

The Bellman-Ford algorithm calculates the shortest paths from a single source vertex to all other vertices in a weighted graph. It's capable of handling graphs with negative edge weights and can detect negative weight cycles.