A Solution to the N-Queens Problem Using Biogeography-Based Optimization
DOI:
https://doi.org/10.9781/ijimai.2017.443Keywords:
Optimization, Evolutionary Algorithm, AlgorithmsAbstract
Biogeography-based Optimization (BBO) is a global optimization algorithm based on population, governed by mathematics of biogeography, and dealing with geographical distribution of biological organisms. The BBO algorithm was used in the present study to provide a solution for the N-queens problem. The performance of the proposed algorithm has been evaluated in terms of the quality of the obtained results, cost function, and execution time. Furthermore, the results of this algorithm were compared against those of genetic and particle swarm algorithms.Downloads
References
Dan Simon, “Biogeography-Based Optimization”, IEEE Transactions on Evolutionary Computation, vol. 12, no. 6, Dec. 2008.
R. L. Haupt and S. E. Haupt, Practical Genetic Algorithms, 2nd Edition, John Wiley & Sons Inc., 2004.
P. Pedregal, Introduction to Optimization, Springer, New York Inc., 2004.
Talbi, El-Ghazali, Metaheuristics: From Design to Implementation, John Wiley and Sons, 2009.
N. Johal, S. Singh, and H. Kundra, “A hybrid FPAB/BBO algorithm for satellite image classification”, International Journal of Computer Applications, vol. 6, no. 5, pp. 31–36, Sep. 2010.
B. Y. Qu, J. J. Liang, and P. N. Suganthan, “Niching particle swarm optimization with local search for multi-modal optimization”, Information Sciences, vol. 197, pp. 131–143, 2012.
M. Ergezer, D. Simon, and D. Du, “Oppositional biogeography-based optimization”, in Proceedings of the IEEE Conference on Systems, Man, and Cybernetics, IEEE, San Antonio, TX, USA, pp. 1035–1040, 2009.
D. Du, D. Simon, and M. Ergezer, “Biogeography-based optimization combined with evolutionary strategy and immigration refusal”, in Proceedings of the IEEE Conference on Systems, Man, and Cybernetics, IEEE, San Antonio, TX, USA, pp. 1023–1028, 2009.
H. Ma, S. Ni, and M. Sun, “Equilibrium species counts and migration model tradeoffs for biogeography-based optimization”, in Proceedings of the 48th IEEE Conference on Decision and Control, IEEE, Shanghai, China, pp. 3306–3310, 2009.
Ivica Martinjak and Marin Golub, “Comparison of Heuristic Algorithms for the N-Queen Problem”, July 2007.
K. D. Crawford, “Solving the N-Queens Problem Using Genetic Algorithms”, in ACM/SIGAPP Symposium on Applied Computing, 1992, pp. 1039–1047.
Aftab Ahmed, Attique Shah, Kamran Ali Sani, and Abdul Hussain Shah Bukhari, “Particle Swarm Optimization for N-Queens Problem”, Journal of Advanced Computer Science and Technology, vol. 1, no. 2, pp. 57–63, 2012.
Amooshahi A., Joudaki M., Imani M., and Mazhari N., “Presenting a New Method Based on Cooperative PSO to Solve Permutation Problems: A Case Study of n-Queen Problem”, in Proceedings of the 3rd International Conference on Electronics Computer Technology, Kanyakumari, India, vol. 4, pp. 218–222, 2011.
Martinjak I. and Golub M., “Comparison of Heuristic Algorithms for the n-Queen Problem”, in Proceedings of the 29th International Conference on Information Technology Interfaces, Cavtat, Croatia, pp. 759–764, 2007.
Draa A., Meshoul S., Talbi H., and Batouche M., “A Quantum-Inspired Differential Evolution Algorithm for Solving the n-Queens Problem”, The International Arab Journal of Information Technology, vol. 7, no. 1, pp. 21–27, 2010.
Shuihua Wang, Yudong Zhang, Genlin Ji, Jiquan Yang, Jianguo Wu, and Ling Wei, “Fruit Classification by Wavelet-Entropy and Feedforward Neural Network Trained by Fitness-Scaled Chaotic ABC and Biogeography-Based Optimization”, Entropy, vol. 17, no. 8, pp. 5711–5728, 2015. doi:10.3390/e17085711.
Mehran Tamjidy, Shahla Paslar, B. T. Hang Tuah Baharudin, Tang Sai Hong, and M. K. A. Ariffin, “Biogeography-based optimization (BBO) algorithm to minimise non-productive time during hole-making process”, International Journal of Production Research, vol. 53, no. 6, 2015.
Vanitha, M., and K. Thanushkodi, “An Effective Biogeography-Based Optimization Algorithm to Solve Economic Load Dispatch Problem”, Journal of Computer Science, vol. 8, no. 9, pp. 1482–1486, 2012.
Shahrzad Saremi and Seyedali Mirjalili, “Integrating Chaos to Biogeography-Based Optimization Algorithm”, International Journal of Computer and Communication Engineering, vol. 2, no. 6, November 2013.
Downloads
Published
-
Abstract38
-
PDF20
Submit Article
Information
