00000ctmaa22000003a 4500 UP-1685675941123923297 Buklod 20230927121513.0 aa rb |||1 u| ta 230927s2010 xx d |||| || | (iLib)UPTAC-00017720408 UPVTC rda eng LG 993.5 2010 C66 H66 Homerez, Arianne N author. SudoCats Cat Swarm Optimization for finding optimal solution of Sodoku puzzles Arianne N. Homerez [and] Ryan Albert B. Raagas. 2010. [4], 110 leaves illustrations Undergraduate thesis (B.S. Computer Science) -- University of the Philippines, Tacloban. Sudoku puzzle problem is one of the NP-Complete problems. The rule of the game is simple and what makes it challenging are the constraints imposed on it and the fact that the solution is a unique one for every puzzle problem. Many algorithms have been applied to efficiently find the optimal solution of the puzzle. The efficiency of the algorithm to solve an optimization problem is tested in Sudoku with its varying level of difficulties applied. In this research, the recent SI algorithm known as Cat Swarm Optimization or CSO algorithm is used to find the optimal solution of a Sudoku puzzle. Inspired from the behavior of cats, previous research shows that this algorithm has shown a better performance compared to Particle Swarm Optimization and weighted-PSO in the cases of function minimization. The CSO algorithm was tested on El Escargot as well as on easy, medium and hard Sudoku puzzles of different sizes. The algorithm was able to solve the puzzle in a small number of cycles in different difficult levels of the Sudoku puzzle except for the 12x12 hard Sudoku although the computational time is longer compared to other approach like ABC.The result indicates that CSO algoritm can be considered as a sufficient approach to solve traditional Sudoku puzzles. Sudoku. Swarm Intelligence. Cat Swarm Optimization. Raagas, Ryan Albert B. author. FI UP UPTAC UPTAC LG 993.5 2010 C66 H66 Thesis