The Ford-Fulkerson algorithm can be adapted to handle graphs with multiple paths and sinks. This adaptability is essential for scenarios where there are multiple ways to route flow from the source to the sink. It involves augmenting the flow along different paths in each iteration until an optimal solution is reached.

The main advantages of using string compression techniques include improved data storage efficiency, reduced bandwidth usage, and faster data transmission. By eliminating repeated characters, the compressed string requires less space, making it beneficial in scenarios with storage or bandwidth constraints.

The longest common substring problem cannot be efficiently solved using the greedy approach. Greedy algorithms make locally optimal choices, and in this problem, a globally optimal solution requires considering the entire input space, making dynamic programming or other techniques more suitable.

To optimize bubble sort, one can implement a flag-based check that stops iterating when no more swaps are needed. This optimization helps in breaking out of the loop early if the array is already sorted, reducing unnecessary iterations and improving the overall efficiency of the algorithm.

The time complexity of DFS in the worst case is O(V + E), where V is the number of vertices and E is the number of edges in the graph. This is because DFS visits each vertex and edge exactly once in the worst case.

In BFS, to avoid infinite loops in graphs with cycles, a queue data structure is used to keep track of visited nodes. The queue ensures that nodes are explored in the order they are discovered, preventing cycles.

The Edit Distance algorithm is used to measure the similarity between two strings by counting the minimum number of operations (insertions, deletions, or substitutions) required to transform one string into the other. It finds applications in spell checking, DNA sequencing, and plagiarism detection.

In DFS, a stack is commonly used to keep track of visited nodes. As the algorithm explores a path as deeply as possible before backtracking, a stack is ideal for maintaining the order of nodes to be visited.

In topological sorting, for every directed edge from vertex u to vertex v, vertex u appears before vertex v in the ordering. This ensures that there is a consistent order of execution for tasks or dependencies.

To find the total number of possible combinations, modify the base case of the dynamic programming solution for the coin change problem. Instead of returning the minimum number of coins, adjust it to return the total number of combinations that make up the target amount.