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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.
| Original language | English |
|---|---|
| Title of host publication | 2022 12th International Conference on Information Science and Technology (ICIST) |
| Publisher | IEEE |
| Pages | 8-17 |
| Number of pages | 10 |
| ISBN (Electronic) | 978-1-6654-8582-1 |
| DOIs | |
| Publication status | Published - 2022 |
| Event | 12th International Conference on Information Science and Technology (ICIST 2022) - Kaifeng, Henan, China Duration: 14 Oct 2022 → 16 Oct 2022 https://conference.cs.cityu.edu.hk/icist/ |
Conference
| Conference | 12th International Conference on Information Science and Technology (ICIST 2022) |
|---|---|
| Country/Territory | China |
| City | Kaifeng, Henan |
| Period | 14/10/22 → 16/10/22 |
| Internet address |
Funding
This work was supported in part by the Research Grants Council of the Hong Kong Special Administrative Region of China under Grants 11202318, 11202019, and 11203721; and in part by the InnoHK initiative, the Government of the Hong Kong Special Administrative Region, and Laboratory for AI-Powered Financial Technologies.
Research Keywords
- Sudoku
- discrete Hopfield network
- Boltzmann machine
- collaborative neurodynamic optimization
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