A Comparative Study: Ant Colony Optimization Algorithm and Backtracking Algorithm for Sudoku Game

Abstract

Combining games with learning methods are the most effective way to increase learning motivation, ratification, concentration, and student skills in understanding and solving problems. One of the most popular games is Sudoku. Traditional methods that have used to solve problems in the Sudoku game show a fairly complex solution. So, a good method for solving these problems is needed such as Ant Colony Optimization, which can be used for path searching. This research uses Ant Colony Optimization as a method to find the best path effectively and efficiently to complete the game. Test results used as a benchmark for the Ant Colony Optimization method are better at completing the game by compiling it with traditional methods such as Backtracking. The result of this research shows that Ant Colony Optimization has better performance than Backtracking algorithm. It was proven by 75 trials conducted at three levels of the game resulting in 67 trials (89%) showing Ant Colony Optimization completing the game faster than Backtracking Algorithm.