In this paper we present a new Ant Colony Optimisation-based algorithm for Sudoku, which out-performs existing methods on large instances. Our method includes a novel anti-stagnation operator, which we call Best Value Evaporation.
In this paper we introduce our new improved version of ant colony optimization genetic algorithm hybrid for Sudoku puzzle solving. Sudoku is combinatorial number puzzle that had become worldwide phenomenon in the last decade. It has also become popular mathematical test problem in order to test new optimization ideas and algorithms for combinatorial problems. In this paper we present our new ideas for populations sorting and elitism rules in order to improve our earlier evolutionary algorithm based Sudoku solvers. Experimental results show that the new ideas significantly improved the speed of Sudoku solving.  Sudoku puzzle is a popular logic game since 2005. This puzzle is an NP-Complete problem which means that a very hard problem that is required deep and efficient algorithm to be solved. So it also draws attention of the scientists to develop methods and algorithm in order to solve Sudoku puzzles. In this study I tried to develop a hybrid algorithm which consist both analytical and heuristic steps to solve the Sudoku puzzle. The developed hybrid algorithm includes two analytical steps and one heuristically step. In the first analytical step, basic manual Sudoku solving methods are used to solve the puzzle. If puzzle is not solved completely than improvement analytical step is applied to solve the puzzle. If still puzzle is not solved completely then heuristic step applied to solve the puzzle completely. In the heuristic step ant colony optimization algorithm (ACO) will be applied to the puzzle. Experiments show that this hybrid ACO algorithm can solve the hardest Sudoku puzzles less than one second. As a result, this study shows that ACO is an effective method that can be applied to solve Sudoku puzzles.