A Collaborative Approach for LA-DHBMO

Vahid  Azadehgan; Payam  Khanteimouri; Sorayya  Akbari; Niusha  Ghandehari

doi:10.4236/am.2012.330203

Vahid Azadehgan, Payam Khanteimouri, Sorayya Akbari, Niusha Ghandehari

Abstract: Honey Bees Mating Optimization (HBMO) is a novel developed method used in different engineering areas. Optimiza-tion process in this algorithm is inspired of natural mating behavior between bees. In this paper, we have attempted to createa new collaborative learning automata based honey bees mating optimization (C-LA-DHBMO).In previous model presented by very authors, the same learning automata parameters for all drones were used. However in the presented method, learning automatas with different reward and penalty parameters have been used which enhance reliability of algorithm and also has high convergence speed compared to previous proposed algorithm (LA-DHBMO). Simulation and comparisons based on several well-studied benchmarks demonstrate the effectiveness, efficiency and robustness of the proposed algorithms.

Keywords: Honey Bees Mating Optimization; Learning Automata; Collaborative Optimization; Function Optimization

Cite this paper: V. Azadehgan, P. Khanteimouri, S. Akbari and N. Ghandehari, "A Collaborative Approach for LA-DHBMO," Applied Mathematics, Vol. 3 No. 10, 2012, pp. 1442-1447. doi: 10.4236/am.2012.330203.

References

[1] V. Azadehgan, A. Sooni, N. Jafarian and D. Khateri, “A New Hybrid Algorithm for Multiobjective Optimization,” 23rd IEEE International Conference on Tools with Artificial Intelligence, Boca Raton, 7-9 November 2011, pp. 911-913. doi:10.1109/ICTAI.2011.152

[2] H. Abbass, “A Monogenous MBO Approach to Satisfiability,” Proceeding of the International Conference on Computational Intelligence for Modelling, Control and Automation, Las Vegas, 2001.

[3] H. Abbass, “Marriage in Honey-Bee Optimization (MBO): A Haplometrosispolygynous Swarming Approach,” The Congress on Evolutionary Computation (CEC2001), Seoul, 27-30 May 2001, pp. 207-214.

[4] A. Afshar, O. Bozog Haddad, M. A. Marino and B. J. Adams, “Honey-Bee Mating Optimization (HBMO) Algorithm for Optimal Reservoir Operation,” Journal of the Franklin Institute, Vol. 344, No. 5, 2007, pp. 452-462. doi:10.1016/j.jfranklin.2006.06.001

[5] M. Fathian, B. Amiri and A. Maroosi, “Application of Honey-Bee Mating Optimization Algorithm on Clustering,” Applied Mathematics and Computation, Vol. 190, No. 2, 2007, pp.1502-1513.

[6] Y. Marinakis and M. Marinaki, “A Honey Bees Mating Optimization Algorithm for the Open Vehicle Routing Problem,” Proceedings of GECCO, 2011, pp. 101-108.

[7] M. A. L. Thathachar and P. S. Sastry, “Varieties of Learning Automata: An Overview,” IEEE Transactions on Systems, Man, and Cybernetics, Part B, Vol. 32, No. 6, 2002, pp. 711-722.

[8] K. S. Narendra and M. A. L. Thathachar, “Learning Automata: An Introduction,” Printice-Hall Inc., Upper Saddle River, 1989.

[9] V. Azadehgan, M. R. Meybodi, N. Jafarian and F. Jafarieh, “Discrete Binary Honey Bees Mating Optimization with Capability of Learning,” Computational Intelligence and Information Technology, Communication in Computer and Information Science, Vol. 250, 2011, pp. 630-636.

[10] D. Karaboga and B. Akay, “A Survey: Algorithms Simulating Bee Swarm Intelligence,” Artificial Intelligence Review, Vol. 31, No .1-4, 2009, pp. 61-85.

[11] X.-S. Yang, “Test problems in optimization,” In: X.-S. Yang, Ed., Engineering Optimization: An Introduction with Metaheuristic Applications, John Wiley & Sons, Hoboken, 2010. doi:10.1002/9780470640425.app1