This is by no means an original project, but it was an excuse to get familiar with SwiftUI. It includes a simple implementation of a minimum priority queue (binary heap). Building a binary heap is probably not a great idea for a production worthy app, as you could most likely find one, but it is a very simple data structure when backed by an array (See MinimumPriorityQueue.swift). Priority queues are a common data structure for solving games where efficiency of moves is important. In the case of a sliding tile puzzle, it can be solved with many sets of moves, but using a minimum priority queue ensures you identify the set of fewest moves in order to solve.
- Create a sliding tile board of any dimension (see
ContentView.swiftandBoard.swift) - Load a sliding tile layout from a text file (see
ContentView.swiftandBoard.swift) - Randomize the board so that at least x% of tiles are out of place. In this case that is 80% but it can be changed in
BoardConstants.swift. - Solve any solvable board and determine if a board is solvable (see
Solver.checkIfSolvable).
Any board layout that has been created from a solvable board, simply by sliding tiles, is solvable. If, however, you were to disassemble a sliding tile board and put the tiles back in, it may not be solvable. If you take a simple board layout like the one below:
1 2 3
4 5 6
7 8 0
and swap any two tiles, then the board becomes unsolvable:
4 2 3
1 5 6
7 8 0
(Here the 1 and 4 have been swapped).
Therefore, to determine if a board is solvable, you can swap any two tiles (not counting the empty slot of course) to create a twin board and run two solvers in lock step. This is obviously not as performant as solving with the assumption that a board can be solved. If the twin board is solved, then by definition the original board cannot be solved.
Automatic solver employs an A* ("A star") algorithm, implemented in Solver.swift leveraging MinimumPriorityQueue.swift.
This method is still quite slow for some large randomized layouts. I'm honestly not certain yet if that indicates a problem or not, but it probably does.
The binary heap is a simple array where a child is always at 2 * i + 1 and a parent is always at (i - 1) / 2. It is a minimum priority queue which means the first element is always the smallest. When a new item is inserted, it is placed at the end of the array and it swims up to its proper position (see swim() function). When a minimum item is removed, then the item from the end of the array (lowest priority) is put in its placed and it sinks down to its proper position (see sink() function). In this case the items put into the priority queue are SearchNode objects which store a reference to a board, total number of moves to get to that board state, and the last move to get to that state. Each board represents an optional move. Search nodes have a priority (that is minimized in the queue) which is an aggregate of the number of moves taken to get there from the original board state and the board's manhattan value.
A single tile's manhattan value is the number of positions it is away from it's final position, and a board's manhattan value is the sum of manhattan values for each tile. Therefore the priority of the search node is a combination of how far this board is away from its end state and how many moves have been made so far. By putting these in a minimum priority queue we are ensuring we get the solution with the minimum number of moves, as opposed to a maximum priority queue which would give us the slowest possible solution (which would be infinite).
- The priority queue, ensuring sinking and swimming of values works properly.
- Board functions that twin a board (for solvability check), finding possibly moves and duplicating boards after potential moves.
- Solver with various board states.
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