Using Intelligent Computational Methods for Optimizing Niching Method
Mohsen Jahanshahi
DOI: 10.4236/ijis.2011.11001   PDF   HTML     5,113 Downloads   12,539 Views   Citations


Optimization implies the minimization or maximization of an objective function. Some problems have sev-eral optimum points which all, should be computed. Niching method is presented to do so. However, its efficiency can be improved via combining it with Memetic algorithm. Therefore, in this paper, Memetic method is used to improve this method in terms of convergence rate and diversity. In the proposed methods, genetic algorithm, PSO, and learning automata are used as a local search algorithm of Memetic method. The result of simulations demonstrates that proposed methods are more effective compared with Niching in terms of convergence and diversity.

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M. Jahanshahi, "Using Intelligent Computational Methods for Optimizing Niching Method," International Journal of Intelligence Science, Vol. 1 No. 1, 2011, pp. 1-7. doi: 10.4236/ijis.2011.11001.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] R. Brits, A. P. Engelbrecht and F. van den Bergh, “A Niching Particle Swarm Optimizer,” Proceedings of the IEEE Swarm Intelligence Symposium, Indianapolis, 24 - 26 April 2003, pp. 54-59,
[2] P. Moscato, “Genetic Algorithms and Martial Arts towards Memetic Algorithm,” Calteth Concurrent Computa-tion Program Report 826, Moscato, 1989.
[3] J. Kennedy and R. C. Eberhart, “Particle Swarm Optimization,” Proceedings of IEEE International Conference on Neural Networks, Piscata-way, 27 November - 1 December 1995, pp. 1942-1948.
[4] M. A. L. Thathachar and P. S. Sastry, “Varieties of Learning Automata: An Overview,” IEEE Transaction on Systems, Man, and Cybernetics-Part B: Cybernetics, Vol. 32, No. 6, 2002, pp. 711-722.
[5] A. Stacey, M. Jancic and I. Grundy, “Particle Swarm Optimization with Mutation,” Proceedings of the Con-gress on Evolutionary Computation, Canberra, 8 - 12 Decem-ber 2003, pp. 1425-1430.
[6] Y. Shi and R. C. Eberhart, “A Modified Particle Swarm Optimizer,” IEEE International con-ference on Evolutionary Computation, Anchorage, 4 - 9 May 1998.
[7] M. Clerc, “Discrete Particle Swarm Optimization,” New Optimization Techniques in Engineering, Springer-Verlag, New York, 2004.
[8] P. Yin, “A Discrete Particle Swarm Algorithm for Optimal Polygonal Approximation of Digital Curves,” Journal of Visual Communication and Image Repre-sentation, Vol. 12, No. 2, 2004, 241-260.
[9] G. Venter and J. Sobieszczanski-Sobieski, “Multidisciplinary Optimization of a Transport Aircraft Wing Using Particle Swarm Optimization,” Structural and Multidisciplinary Optimization, Vol. 26, No. 1, 2004, pp. 121-131.
[10] G. C. Onwubolu and M. Clerc, “Op-timal Path for Automated Drilling Operations by a New Heu-ristic Approach Using Particle Swarm Optimization,” Interna-tional Journal of Production Research, Vol. 4, No. 3, 2004, pp. 473-491. doi:10.1080/00207540310001614150
[11] F. Bergh and A. P. Engelbrecht, “A New Locally Convergent Particle Swarm Optimizer,” IEEE International Conference on Systems, Man and Cybernetics, Tunisia, 2002.
[12] T. I. Zohdi, “Com-putational Design of Swarms,” International Journal for Nu-merical Methods in Engineering, Vol. 57, No. 15, 2003, pp. 2205-2219. doi:10.1002/nme.762
[13] M. Jahanshahi, M. R. Meybodi and M. Dehghan, “Cellular Learning Automata Based Scheduling Method for Wireless Sensor Networks,” Proceedings of the 14th International CSI Computer Conference (CSICC’09), Amirkabir University of Technology, Tehran, October 20-21, 2009, pp. 646-651.

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