Proceedings of the 2013 International Symposium on Nonlinear Theory and its Applications
2013
Session Number:B1L-B
Session:
Number:193
A Fission-and-Recombination Particle Swarm Optimizer
Takumi Sato, Kazuki Maruyama, Toshimichi Saito,
pp.193-196
Publication Date:
Online ISSN:2188-5079
[1] A. P. Engelbrecht, Fundamentals of computational swarm intelligence, Willey, 2005.
[2] M. P. Wachowiak, R. Smolikova, Y. Zheng, J. M. Zurada and, A. S. Elmaghraby, An approach to multimodal biomedical image registration utilizing particle swarm optimization, IEEE Trans. Evol. Comput., 8, 3. pp. 289-301, 2004.
[3] A. B. van Wyk and A. P. Engelbrecht, Overfitting by PSO trained feedforward neural networks, in Proc. IEEE Congress Evol. Comput., pp. 2672-2679, 2010.
[4] H. Matsushita and T. Saito, Application of particle swarm optimization to parameter search in dynamical systems, NOLTA, IEICE, E94-N, 10, pp. 458-471, 2011.
[5] Z. Sevkli and F. E. Sevilgen, Discrete particle swarm optimization for the orienteering problem, in Proc. IEEE Congress Evol. Comput., pp. 1937-1944, 2010.
[6] M. Kubota and T. Saito, A discrete particle swarm optimizer for multi-solution problems, IEICE Trans. Fundamentals, E95-A, 1, pp. 406-409, 2012.
[7] K.-B. Lee and J.-H. Kim, Mass-spring-damper motion dynamics-based particle swarm optimization, in Proc. IEEE Congress Evol. Comput., pp. 2348-2353, 2008.
[8] T. Saito and E. Miyagawa, Growing-tree particle swarm optimizer with simple tabu search function, Proc. of NOLTA, pp. 376-379, 2009.
[9] R. Sano, T. Shindo, K. Jin'no, and T. Saito, PSO-based multiple optima search systems with switched topology, in Proc. IEEE Congress Evol. Comput., pp. 3301-3307, 2012.
[10] K. Maruyama, R. Sano and T. Saito, Deterministic discrete particle swarm optimizers with collision and insensitivity, Proc. NDES, pp. 245-248, 2012.