A Novel Hybrid Approach to N-Queen Problem

  • Kavishi Agarwal
  • Akshita Sinha
  • M. Hima Bindu
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 166)


These days computers deals with highly intricate problems. This paper also discusses such kind of complex problems called Constraint Satisfaction Problems (CSP). These problems have a set of variables, domain from which a variable takes its value and a set of constraints applied on these variables. For example N-Queen problem, timetabling problem, scheduling problem etc. are CSPs. In practical scenario, it is unlikely to obtain a solution that satisfies all constraints or most of the constraints. Such a solution is an exact solution. Even if an exact algorithm can be developed its time or space complexity may turnout unacceptable. In reality, it is often sufficient to find an approximate or partial solution to such NP problems using heuristic algorithms. Heuristic methods are used to speed up the process of finding a satisfactory solution, where an exhaustive search is impractical; hence resulting in guaranteed and approximate solutions. This paper proposes an efficient hybrid solution for standard N-Queen problem using Ant Colony Optimization and Genetic Algorithm. It also compares the performances of classical backtrack and brute force methods and heuristic methods, Simulated annealing and Genetic algorithm on N-Queen problem.


Constraint Satisfaction Problems N-Queen Hybridized Heuristic Ant Colony Optimization (ACO) Genetic Algorithm (GA) 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Khan, S., Bilal, M., Sharif, M., Sajid, M., Baig, R.: Solution of n-Queen Problem Using ACO. IEEE (2009)Google Scholar
  2. 2.
    Crawford, K.D.: Solving the N-Queens Problem Using Genetic Algorithms. In: Proceedings ACM/SIGAPP Symposium on Applied Computing, Kansas City, pp. 1039–1047 (1992)Google Scholar
  3. 3.
    Božikovic, M., Golub, M., Budin, L.: Solving n-Queen problem using global parallel genetic algorithm. In: EUROCON, Ljubljana, Slovenia (2003)Google Scholar
  4. 4.
    Martinjak, I., Golub, M.: Comparison of Heuristic Algorithms for the N-Queen Problem. In: Proceedings of the ITI 2007 29th Int. Conf. on Information Technology Interfaces, June 25-28 (2007)Google Scholar
  5. 5.
    Thanh, N.D.: Solving Timetabling Problem Using Genetic and Heuristic Algorithms. In: Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing. IEEE (2007)Google Scholar
  6. 6.
    Kokash, N.: An introduction to heuristic algorithms. Department of Informatics and TelecommunicationsGoogle Scholar
  7. 7.
    Cormen, T., Leiserson, C., Rivest, R.: Introduction to algorithms. MIT Press (1989)Google Scholar
  8. 8.
    Horowitz, E., Sahni, S.: Fundamentals of computer algorithms. Computer Science Press Inc., Rockville (1978)Google Scholar
  9. 9.
    Crawford, K.D.: Solving n-Queen problem using genetic algorithms. Tulsa UniversityGoogle Scholar
  10. 10.
    Brailsford, S.C., Potts, C.N., Smith, B.M.: Constraint satisfaction problems: Algorithms and applications. European Journal of Operational Research 119, 557–581 (1998)CrossRefGoogle Scholar
  11. 11.
    Khajehzadeh, M., Taha, M.R., El-Shafie, A., Eslami, M.: A Survey on Meta-Heuristic Global Optimization Algorithms. Research Journal of Applied Sciences, Engineering and Technology (June 25, 2011) ISSN 2040-7467Google Scholar

Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Jaypee Institute of Information TechnologyNoidaIndia

Personalised recommendations