Stochastic Optimization Approaches for Solving Sudoku

TL;DR

This paper explores stochastic search techniques like CGA, RPSO, QSA, and HGASA to solve Sudoku puzzles, highlighting the effectiveness of hybrid and quantum-inspired methods over others.

Contribution

It introduces and evaluates multiple stochastic algorithms, including a novel hybrid approach, for efficiently solving Sudoku puzzles.

Findings

CGA solved Sudoku in 28 seconds

QSA solved Sudoku in 65 seconds

HGASA solved Sudoku in 1.447 seconds

Abstract

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.