Summary

Proceedings of the 2013 International Symposium on Nonlinear Theory and its Applications

2013

Session Number:B3L-B

Session:

Number:306

A Study on Stochastic Animal Swarm Optimization with gradient estimation methods

Takeshi Uchitane,  Taro Fukutomi,  Toshiharu Hatanaka,  Atsushi Yagi,  

pp.306-309

Publication Date:

Online ISSN:2188-5079

DOI:10.15248/proc.2.306

PDF download (1.2MB)

Summary:
In this paper, we addressed a search algorithm based on a mathematical swarming model described by stochastic differential equations. The swarm model is constructed by the attractive and repulsive forces as the relationship among search points that imitated animate beings. Numerical simulations are performed to shown availabilities of the proposed method for function optimization.

References:

[1] M. Dorigo, and L. Gambardella, “Ant Colony Systems: A Cooperative Learning Approach to the Traveling Salesman Problem,” IEEE Transactions on Evolutionary Computation, Vol. 1, No. 1, pp 53-66, 1997.

[2] Dervis Karaboga, Bahriye Basturk, “A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm,” Journal of Global Optimization, Vol. 39, No. 3, pp.459-471, 2007.

[3] Passino, K.M., “Biomimicry of bacterial foraging for distributed optimization and control,” IEEE Control Systems, Vol.—22, No. 3, pp. 52-67, 2002.

[4] J. Kennedy, and R. Eberhart,“Particle Swarm optimization,” Proceedings of IEEE International Con-ference on Neural Networks, pp. 1942-1948, 1995.

[5] Craig W. Reynolds, “Flocks, Herds, and Schools: A Distributed Behavioral Model, in Computer Graphics,” Proceedings of the 14th annual conference on Computer graphics and interactive techniques, pp. 25-34, 1987.

[6] T. Uchitane, T. V. Ton and A. Yagi, “An ordinary differential equation model for fish schooling,” Scientiae Mathmaticae Japonicae, 2013.

[7] Y. Maeda, “Simultaneous Perturbation Optimization Methods and Their Applications,” Institute of System, Control and Information Engineers, Vol. 52. No. 2, pp. 47-53, 2008.(in Japanese)

[8] D. S. Broomhead and D. Lowe, “Radial Basis Functions, Multi-Variable Functional Interpolation and Adaptive Networks,” Royal Signals and Radar Estab-lishment, No. 4148, 1988.