• E-ISSN:

    2454-9584

    P-ISSN

    2454-8111

    Impact Factor 2021

    5.610

    Impact Factor 2022

    6.247

  • E-ISSN:

    2454-9584

    P-ISSN

    2454-8111

    Impact Factor 2021

    5.610

    Impact Factor 2022

    6.247

  • E-ISSN:

    2454-9584

    P-ISSN

    2454-8111

    Impact Factor 2021

    5.610

    Impact Factor 2022

    6.247

INTERNATIONAL JOURNAL OF INVENTIONS IN ENGINEERING & SCIENCE TECHNOLOGY

International Peer Reviewed (Refereed), Open Access Research Journal
(By Aryavart International University, India)

Paper Details

A Novel Approach Of Solving Classical N-Queens Problem Using Simulated Annealing With Genetic Operators

Sri Sai Devi Bhagavan Sidhvik Suhas Alladaboina

IIT Mumbai

1 - 7 Vol. 10, Jan-Dec, 2024
Receiving Date: 2024-01-03;    Acceptance Date: 2024-02-22;    Publication Date: 2024-03-06
Download PDF

Abstract

Developing a novel algorithm for a extensively researched issue such as the N Queens problem, in cases where no analogous algorithm has been documented in existing literature, can pose a significant challenge. The proposed paper presents a novel approach that combines some existing techniques in a unique way to potentially achieve a different perspective on solving the problem. This approach combines simulated annealing, a probabilistic optimization technique, with genetic operators to explore the solution space in a distinct manner. It's worth mentioning that the amalgamation and execution of existing techniques in this approach might introduce a novel perspective.

Keywords: N Queens Problem; Simulated Annealing and Genetic Operators

    References

  1. Das, S., & Konar, A. (2009). Solving the N-Queens Problem with a Hybrid Cellular Genetic Algorithm. International Journal of Hybrid Intelligent Systems, 6(3), 155-167.
  2. Zhang, J., & Mühlenbein, H. (2002). The Self-Adaptive Genetic Algorithm for Multi-Objective Optimization with Constraints. Evolutionary Computation, 10(1), 44-72.
  3. Michalewicz, Z., & Fogel, D. B. (2000). How to Solve It: Modern Heuristics. Springer Science & Business Media.
  4. Aarts, E. H. L., & Korst, J. H. M. (1989). Simulated Annealing and Boltzmann Machines: A Stochastic Approach to Combinatorial Optimization and Neural Computing. John Wiley & Sons.
  5. Srinivasan, D., & Ganesan, K. (2017). Solving the N-Queens Problem Using Genetic Algorithm with Simulated Annealing Crossover Operator. Procedia Computer Science, 115, 188-195.
  6. Ficco, M., Lanzotti, L., & Mazzarese, D. (2014). Solving the N-Queens Problem Using a Hybrid Genetic Algorithm with Simulated Annealing. Procedia Computer Science, 32, 870-877.
  7. Ficco, M., Lanzotti, L., & Mazzarese, D. (2015). An Efficient Parallel Hybrid Algorithm to Solve the N-Queens Problem. Journal of Computational Science, 8, 68-76.
  8. Biswas, A. R., Chakraborty, U. K., & Mandal, D. (2011). Simulated Annealing Based Genetic Algorithm for Solving N-Queens Problem. International Journal of Computer Applications, 20(9), 13-18.
  9. Nguyen, H. H., & Nguyen, H. A. (2017). A New Hybrid Simulated Annealing Genetic Algorithm for Solving N-Queens Problems. Journal of Computer Science and Cybernetics, 33(4), 327-337.
Back