The Fifteen Puzzle- A New Approach through Hybridizing Three Heuristics Methods
This work addresses the computational challenge of solving the Fifteen Puzzle, an incremental improvement for puzzle-solving algorithms.
The paper tackles the Fifteen Puzzle problem by hybridizing three heuristics (Manhattan distance, linear conflict, and walking distance) with Bidirectional A* search to reduce the number of generated states, achieving significant reductions in space complexity while guaranteeing optimal or near-optimal solutions.
Fifteen Puzzle problem is one of the most classical problems that have captivated mathematical enthusiasts for centuries. This is mainly because of the huge size of the state space with approximately 1013 states that have to be explored and several algorithms have been applied to solve the Fifteen Puzzle instances. In this paper, to deal with this large state space, Bidirectional A* (BA*) search algorithm with three heuristics, such as Manhattan distance (MD), linear conflict (LC), and walking distance (WD) has been used to solve the Fifteen Puzzle problems. The three mentioned heuristics will be hybridized in a way that can dramatically reduce the number of generated states by the algorithm. Moreover, all those heuristics require only 25KB of storage but help the algorithm effectively reduce the number of generated states and expand fewer nodes. Our implementation of BA* search can significantly reduce the space complexity, and guarantee either optimal or near-optimal solutions.1