Particle Swarm Optimization For N-Queens Problem
Abstract
The N-Queen is one of the prominent toy problems for performance assessment in the domain of Computational Intelligence. The problem can examine the diverse aspects of solving techniques, counting the potential of obtaining feasible solution and time & space complexity. The well-defined set of constraint sketches out the overall problem. The Queen has various attacking options over the chessboard. In point of fact, it can walk off across the column, row and two diagonal. The feasible solution of N-Queens demands the non-attacking placements over the chessboard. This research inspects the effectiveness of Particle Swarm Optimization in order to solve the problem. In PSO, the particle contains a structured set of Queen Placements. Current position of particle is well directed by the pbest and gbest parameters, each new generation obtains more converged set of particles. The partial but improved outcome promotes and furnishes to succeeding generation while the substandard may be discarded. The investigational results make known the dynamic features of PSO and eventually validate the effectiveness of research direction.
Keywords: N-Queens problem, PSO, Constraints.
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References
[1] J. Kennedy and R. C. Eberhart, "Particle swarm optimization," in Proceedings of the IEEE International Conference on Neural Networks, 1995, pp. 1942-1948.
[2] Aftab Ahmed, et al., "Constraint Logic Programing For Solving N-Queens Problem," in Second International Conference on Computer & Emerging Technologies (ICCET 2012), Shah Abdul Latif University, 2012, pp. 15-19.
[3] K. D. Crawford, "Solving the N-Queens Problem Using Genetic Algorithms," in ACM/SIGAPP Symposium on Applied Computing, 1992, pp. 039-1047.
[4] Aftab Ahmed, et al., "Solving University Scheduling Problem Using Hybrid Approach," Mehran University Research Journal of Engineering & Technology vol. 30, pp. 613-624, October 2011.
[5] S. Russell and P. Norvig, Artificial Intelligence A Modern Approach Prentice Hall, 2003.
View more references (2)
[6] Aftab Ahmed, et al., "Hyper-GA for Solving Benchmark Scheduling Problems," Australian Journal of Basic and Applied Sciences, vol. 5, pp. 1657-1667, June 2011.
[7] Lixi Zhang and S. Lau, "Constructing university timetable using constraint satisfaction programming approach," in International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce, 2005, pp. 55-60.