About this Journal Submit a Manuscript Table of Contents
The Scientific World Journal
Volume 2013 (2013), Article ID 597803, 6 pages
http://dx.doi.org/10.1155/2013/597803
Research Article

A Novel Complex Valued Cuckoo Search Algorithm

1College of Information Science and Engineering, Guangxi University for Nationalities, Nanning 530006, China
2Guangxi Key Laboratory of Hybrid Computation and IC Design Analysis, Nanning 530006, China

Received 3 March 2013; Accepted 8 May 2013

Academic Editors: P. Agarwal, V. Bhatnagar, and Y. Zhang

Copyright © 2013 Yongquan Zhou and Hongqing Zheng. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. X. S. Yang and S. Deb, “Cuckoo search via Lévy flights,” in Proceedings of the World Congress on Nature and Biologically Inspired Computing (NABIC '09), pp. 210–214, IEEE, December 2009. View at Publisher · View at Google Scholar · View at Scopus
  2. X. S. Yang and S. Deb, “Engineering optimization by cuckoo search,” International Journal of Mathematical Modelling and Numerical Optimization, vol. 4, pp. 330–343, 2010.
  3. D. Casasent and S. Natarajan, “A classifier neural net with complex-valued weights and square-law nonlinearities,” Neural Networks, vol. 8, no. 6, pp. 989–998, 1995. View at Publisher · View at Google Scholar · View at Scopus
  4. Z. H. Zheng, Y. Zhang, and Y. H. Qiu, “Genetic algorithm based on complex-valued encoding,” Control Theory & Applications, vol. 20, no. 1, pp. 97–100, 2003.
  5. D. B. Chen, H. J. Li, and Z. Li, “Particle swarm optimization based on complex-valued encoding and application in function optimization,” Computer and Applications, vol. 45, no. 10, pp. 59–61, 2009.
  6. X. S. Yang and S. Deb, “Multiobjective cuckoo search for design optimization,” Computer ' Operations Research, vol. 40, no. 6, pp. 1616–1624, 2013. View at Publisher · View at Google Scholar
  7. X. Liu, “Improved particle swarm optimization and its application in PID parameters optimization,” Electronic Design Engineering, vol. 19, no. 9, pp. 79–82, 2011.
  8. C. L. Zhang, J. L. Jiang, S. H. Jiang, and Q. Li, “Adaptive hybrid particle swarm optimization algorithm and application,” Application Research of Computers, vol. 28, no. 5, pp. 1696–1698, 2011.