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2026-09-13T14:16:23.653Z
LKSlide IDA* (Manhattan + linear conflict)
[ { "board": "3x3", "mode": "optimal (W=1)", "scrambleDepth": 10, "games": 200, "meanMoves": 9.6, "medianMoves": 10, "meanNodes": 22, "medianNodes": 18 }, { "board": "3x3", "mode": "optimal (W=1)", "scrambleDepth": 20, "games": 200, "meanMoves": 17.2, "media...

Sliding Puzzle Solver Benchmark — Solution Length & Search Nodes

Optimal (and near-optimal) solution length and search effort for the LK Forge Sliding Puzzle Solver, which uses IDA* with a Manhattan-distance + linear-conflict heuristic. The 8-puzzle (3×3) is solved optimally (weight = 1); the 15-puzzle (4×4) uses a lightly-weighted, near-optimal search. Boards are generated by a seeded random walk of the blank from the goal.

Headline numbers

8-puzzle (3×3), optimal, 200 boards per scramble depth:

Scramble depth Mean optimal moves Mean search nodes
10 9.6 22
20 17.2 353
30 20.2 962
40 21.6 1,373
50 21.4 1,500

Past 40 random moves the board is effectively fully scrambled and the optimal solution length converges to ~22 moves — matching the known average optimal solution length of the 8-puzzle (21.97 over all solvable states). Search nodes plateau because the state is already random.

15-puzzle (4×4), near-optimal autoSolve, 200-move scrambles: mean 53 moves, ~232k nodes.

Files

  • data/sliding-puzzle-benchmark.json — per board size / scramble depth: mean & median moves and search nodes.

Method

  • Engine: the shipped sliding-solver-engine.js (LKSlide), used unmodified.
  • Node = one IDA* expansion (machine-independent measure of search effort).
  • Boards generated by seeded random walk (always solvable), verified with the parity test.

Reproduce

node scripts/benchmark-sliding-puzzle.mjs

Citation

LK Forge (2026). Sliding Puzzle Solver Benchmark — Solution Length & Search Nodes. https://lkforge.com/tools/puzzles/sliding-puzzle-solver/

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