Stochastic Optimization Approaches for Solving Sudoku

Details Stochastic Optimization Approaches for Solving Sudoku Stochastic Optimization Approaches for Solving Sudoku

2008-05-06

arxiv.org/abs/0805.0697v1

Computer Science

Neural and Evolutionary Computing

13 pages

Scientific paper

10.1016/j.eswa.2012.04.019

In this paper the Sudoku problem is solved using stochastic search techniques and these are: Cultural Genetic Algorithm (CGA), Repulsive Particle Swarm Optimization (RPSO), Quantum Simulated Annealing (QSA) and the Hybrid method that combines Genetic Algorithm with Simulated Annealing (HGASA). The results obtained show that the CGA, QSA and HGASA are able to solve the Sudoku puzzle with CGA finding a solution in 28 seconds, while QSA finding a solution in 65 seconds and HGASA in 1.447 seconds. This is mainly because HGASA combines the parallel searching of GA with the flexibility of SA. The RPSO was found to be unable to solve the puzzle.

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