TARU PUBLICATIONS
Journal of Discrete Mathematical Sciences and Cryptography cover
Open Access ·Peer-reviewed·ISSN (Online): 2169-0065·ISSN (Print): 0972-0529

Monthly Journal: Publishes theoretical and applied research in all areas of Discrete Mathematical Sciences, Cryptography, Combinatorics, Elliptic Curves and Information Security.

Issues up to 2022 co-published with and available at:Taylor & Francis Online
submissions@tarupublications.com
Open Access Research Article

Exploring n-Queens problem solutions through advanced combinatorial and discrete mathematical techniques

, , , , , *

* Corresponding author · click or hover a name for details

pp. 523–532Vol. 29Issue 2-AFebruary 2026DOI: 10.47974/JDMSC-2492 Crossmark XML
Received:
08 Apr 2025
Published Online:
31 Dec 2025
Article type:
Research Article
Language:
EN
Article no.:
JDMSC-2492
Pages:
523–532

Abstract

The n-Queens problem involves positioning n chess queens on an n×n board without any of them threatening one other. Typical combinatorial issue in computer math. This topic has been studied in graph theory, optimization, and calculation difficulty. Modern combinatorial and discrete arithmetic methods like department-and-bound algorithms, constraint delight approaches, and optimization equipment are better at solving the n-Queens problem, however going backwards has often worked. Sparkling advancements in mathematical frameworks, diagram colouring, and combinatorial designs have added ways to handle huge n. In this study, mathematical methods are tested with computers and compared to the n-Queens problem. The research also covers the problem in higher-dimensional settings and investigates its relevance to aid allocation and timetable construction. The results illuminate combinatorial problem-solving methods in computer mathematics.

Keywords

Subject Classifications

05C20

References

[1] F. A. Hashim, K. Hussain, E. H. Houssein, M. S. Mabrouk, and W. Al-Atabany, “Archimedes optimization algorithm: A new metaheuristic algorithm for solving optimization problems,” Appl. Intell., vol. 51, pp. 1531–1551 (2021).
[2] A. K. Shukla, D. Tripathi, B. R. Reddy, and D. Chandramohan, “A study on metaheuristics approaches for gene selection in microarray data: Algorithms, applications and open challenges,” Evol. Intell., vol. 13, pp. 309–329 (2020).
[3] M. U. Ronque, R. M. Feitosa, and P. S. Oliveira, “Natural history and ecology of fungus-farming ants: A field study in Atlantic rainforest,” Insectes Soc., vol. 66, pp. 375–387 (2019).
[4] Á. S. Aguilar-Colorado and J. Rivera-Chávez, “Ants/nest-associated fungi and their specialized metabolites: Taxonomy, chemistry, and bioactivity,” Rev. Bras. Farmacogn., vol. 33, pp. 901–923 (2023).
[5] E. K. Moghadam, M. Vahdanjoo, A. L. Jensen, M. Sharifi, and C. A. G. Sørensen, “An arable field for benchmarking of metaheuristic algorithms for capacitated coverage path planning problems,” Agronomy, vol. 10, pp. 1454 (2020).
[6] A. Kaveh and Y. Vazirinia, “Construction site layout planning problem using metaheuristic algorithms: A comparative study,” Iran. J. Sci. Technol. Trans. Civ. Eng., vol. 43, pp. 105–115 (2019).
[7] S. Reynolds and H. Mehta, “AI-driven war probability prediction using big data analysis of real and fake news,” Int. J. Electr., Electron. Comput. Syst., vol. 14, no. 1, pp. 41–46 (2025).
[8] A. G. Hussien, A. E. Hassanien, E. H. Houssein, and M. Amin, “New binary whale optimization algorithm for discrete optimization problems,” Eng. Optim., vol. 52, pp. 945–959 (2020).
[9] A. Asghari and M. K. Sohrabi, “Server placement in mobile cloud computing: A comprehensive survey for edge computing, fog computing and cloudlet,” Comput. Sci. Rev., vol. 51, pp. 100616 (2024).
[10] J. N. Avadhesh, H. Dave, G. P. Shravan, G. S. Zulfikar, and M. A. Akhilesh, “A blockchain-based voting system for Grampanchayat,” Int. J. Adv. Electr. Comput. Eng., vol. 14, no. 1, pp. 76–84 (2025).
[11] A. Asghari and M. K. Sohrabi, “Multiobjective edge server placement in mobile-edge computing using a combination of multiagent deep Q-network and coral reefs optimization,” IEEE Internet Things J., vol. 9, pp. 17503–17512 (2022).
[12] V. V. Raje, S. S. Dari, S. N. Bamne, P. B. Chaudhari, T. S. Bhosale, G. Khandelwal, and V. Khetani, “Efficient resource allocation in healthcare systems through deep learning and optimization science,” J. Inf. Optim. Sci., vol. 46, no. 4-B, pp. 1277–1288 (2025).

Views: 232Downloads: 155Citations: 0