Abstract
Solving logic puzzles using specific algorithms presents an intriguing challenge where the efficiency of the approaches is crucial. One such puzzle involves solving nonograms, where the task is to fill in board fields according to the conditions specified for each row and column. The availability of various methods allows for comparing their efficiency and effectiveness. This study aimed to evaluate the effectiveness of two algorithms from different categories. We selected a modified Depth-First Search (DFS) method and a soft computing method based on permutations generation to solve a set of chosen nonograms. The research was conducted using four different board sizes, and the results indicated that the effectiveness of the methods largely depends on the complexity of the nonogram. The algorithm employing permutations consistently produced stable results, while the DFS method did not always guarantee a complete solution.
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