Calculate minimum moves to solve a puzzle
I'm in the process of creating a game where the user will be presented with 2 sets of colored tiles. In order to ensure that the puzzle is solvable, I start with one set, copy it to a second set, then swap tiles from one set to another. Currently, (and this is where my issue lies) the number of swaps is determined by the level the user is playing - 1 swap for level 1, 2 swaps for level 2, etc. This same number of swaps is used as a goal in the game. The user must complete the puzzle by swapping a tile from one set to the other to make the 2 sets match (by color). The order of the tiles in the (user) solved puzzle doesn't matter as long as the 2 sets match.
The problem I have is that as the number of swaps I used to generate the puzzle approaches the number of tiles in each set, the puzzle becomes easier to solve. Basically, you can just drag from one set in whatever order you need for the second set and solve the puzzle with plenty of moves left. What I am looking to do is after I finish building the puzzle, calculate the minimum number of moves required to solve the puzzle. Again, this is almost always less than the number of swaps used to create the puzzle, especially as the number of swaps approaches the number of tiles in each set.
My goal is to calculate the best case scenario and then give the user a "fudge factor" (ie 1.2 times the minimum number of moves). Solving the puzzle in under this number of moves will result in passing the level.
A little background as to how I currently have the game configured:
Levels 1 to 10: 9 tiles in each set. 5 different color tiles. Levels 11 to 20: 12 tiles in each set. 7 different color tiles. Levels 21 to 25: 15 tiles in each set. 10 different color tiles.
Swapping within a set is not allowed.
For each level, there will be at least 2 tiles of a given color (one for each set in the solved puzzle).
Is there any type of algorithm anyone could recommend to calculate the minimum number of moves to solve a given puzzle?
The minimum moves to solve a puzzle is essentially the shortest path from that unsolved state to a solved state. Your game implicitly defines a graph where the vertices are legal states, and there's an edge between two states if there's a legal move that enables that transition.
Depending on the size of your search space, a simple breadth-first search would be feasible, and would give you the minimum number of steps to reach any given state. In fact, you can generate the problems this way too: instead of making random moves to arrive at a state and checking its "distance" from the initial state, simply explore the search space in breadth-first/level-order, and pick a state at a given "distance" for your puzzle.
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IF the search space is too huge for BFS (and I'm not yet convinced that it is), you can use iterative deepening depth-first search instead. It's space-efficient like DFS, but (cummulatively) level-order like BFS. Even though nodes would be visited many times, it is still asymptotically identical to BFS, but requiring much leser space.
我不太了解你描述中的难题，但是解决这类难题通常有用的两个一般想法是回溯和分支和界限。
The A* search algorithm. The idea is that you have some measure of how close a position is to the solution. A* is then a "best first" search in the sense that at each step it considers moves from the best position found so far. It's up to you to come up with some kind of measure of how close you are to a solution. (It doesn't have to be accurate, it's just a heuristic to guide the search.) In practice it often performs much better than a pure breadth first search because it's always guided by your closeness scoring function. But without understanding your problem description, it's hard to say. (A rule of thumb is that if there's a sense of "making progress" while doing a puzzle, rather than it all suddenly coming together at the end, then A* is a good choice.)
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