Dijkstra's, Bellman-Ford, and Floyd-Warshall algorithms are three different methods used to find the shortest paths in graphs. Each one is good for different types of problems.

Dijkstra's Algorithm
Dijkstra's algorithm works well with graphs that only have positive weights. It uses a special list (called a priority queue) to keep track of the closest unvisited points. Once it finds the shortest path to a point, that path is considered the best. This method is quick, running in a time of $O(E + V \log V)$ when using a binary heap. This makes it great for graphs that aren’t too crowded with points. However, it does not work with edges that have negative weights, which limits when we can use it.

Bellman-Ford Algorithm
The Bellman-Ford algorithm is different because it can handle negative weights. It goes through the graph step by step, checking all edges and repeating the process $V-1$ times, where $V$ is the number of points (or vertices). This allows it to find out if there are negative weight cycles. Its time complexity is $O(V \cdot E)$, which means it can be slower for large graphs, but it can solve a wider variety of problems compared to Dijkstra's.

Floyd-Warshall Algorithm
The Floyd-Warshall algorithm approaches the problem in another way. It looks for the shortest paths between all pairs of points. This method uses a technique called dynamic programming. It checks every pair of points and updates the distances based on other points in-between. The time complexity is $O(V^3)$, which makes it best for smaller graphs. It can also identify negative cycles, making it quite useful.

In short, Dijkstra's algorithm is fast and works with only positive weights, Bellman-Ford is flexible and can deal with negative weights, and Floyd-Warshall gives an all-around solution for reaching every pair of points. Each of these algorithms has its own strengths and is used for different situations when finding the shortest paths.