COMPARISON OF HEURISTIC SEARCH ALGORITHMS IN SOLVING 11-PUZZLE PROBLEMS

  • Department of Computer Science, University of Sri Jayewardenepura, Nugegoda, Sri Lanka.
  • Apple Research and Development Centre, Department of Computer Science, Faculty of Applied Sciences, University of Sri Jayewardenepura, Nugegoda, Sri Lanka.
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This paper presents a comparative analysis of the A and Iterative Deepening A (IDA) search algorithms to solve the 11-puzzle problems using the Manhattan distance heuristic.Both algorithms were implemented and evaluated based on performance metrics including nodes generated, nodesexpanded, solution depth, effective branching factor, and CPU time. The results indicate that A consistently outperforms IDAin computational efficiency and scalability with A reducing the node generation by 62.86%, node expansion by 61.60%, and CPU time by 51.46%, though IDA* remains more memory efficient. These findings validate the broader applicability of heuristic search strategies and reinforce the role of the Manhattan distance heuristic in optimal path finding.

W.M.A.C. Linara and D.D.A. Gamini (2026); COMPARISON OF HEURISTIC SEARCH ALGORITHMS IN SOLVING 11-PUZZLE PROBLEMS, Int. J. of Adv. Res. (Feb), ISSN 2320-5407. DOI URL: https://dx.doi.org/

D.D.A. Gamini
Apple Research and Development Centre, Department of Computer Science, Faculty of Applied Sciences, University of Sri Jayewardenepura, Nugegoda, Sri Lanka
Sri Lanka

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