Collaborative Neurodynamic Algorithms for Solving Sudoku Puzzles

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

1 Scopus Citations
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Detail(s)

Original languageEnglish
Title of host publication2022 12th International Conference on Information Science and Technology (ICIST)
PublisherInstitute of Electrical and Electronics Engineers, Inc.
Pages8-17
Number of pages10
ISBN (electronic)978-1-6654-8582-1
Publication statusPublished - 2022

Conference

Title12th International Conference on Information Science and Technology (ICIST 2022)
Location
PlaceChina
CityKaifeng, Henan
Period14 - 16 October 2022

Abstract

In this article, Sudoku is formulated as a quadratic unconstrained binary optimization, and a variables reduction algorithm is proposed based on given elements. Collaborative neurodynamic optimization algorithms based on discrete Hopfield networks or Boltzmann machines are developed for solving the formulated optimization problem. A population of discrete Hopfield networks or Boltzmann machines operating concurrently are employed for scatter search. A particle swarm optimization rule is used to re-initialize the initial states of discrete Hopfield networks or Boltzmann machines upon their local convergence. Experimental results on five Sudoku instances are elaborated to demonstrate the efficacy of the proposed collaborative neurodynamic optimization algorithms for solving Sudoku puzzles.

Research Area(s)

  • Sudoku, discrete Hopfield network, Boltzmann machine, collaborative neurodynamic optimization

Citation Format(s)

Collaborative Neurodynamic Algorithms for Solving Sudoku Puzzles. / Li, Hongzong; Wang, Jun.
2022 12th International Conference on Information Science and Technology (ICIST). Institute of Electrical and Electronics Engineers, Inc., 2022. p. 8-17.

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review