Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering
Volume 2013 (2013), Article ID 108768, 9 pages
http://dx.doi.org/10.1155/2013/108768
Research Article

LGMS-FOA: An Improved Fruit Fly Optimization Algorithm for Solving Optimization Problems

School of Economics and Business Administration, Chongqing University, Chongqing 400030, China

Received 16 May 2013; Revised 1 August 2013; Accepted 18 August 2013

Academic Editor: Yudong Zhang

Copyright © 2013 Dan Shan et al. 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. Y. D. Bertrand and D. Braba, “Feature selection by a genetic algorithm application to seed discrimination by artificial vision,” Journal of the Science of Food and Agriculture, vol. 76, pp. 77–86, 1998. View at Google Scholar
  2. Y.-J. Lei, S.-W. Zhang, X.-W. Li, and C.-M. Zhou, Matlab Genetic Algorithm Toolbox and Its Application, Xidian University Publishing House, Xi'an, China, 2005.
  3. M. B. Aryanezhad and M. Hemati, “A new genetic algorithm for solving nonconvex nonlinear programming problems,” Applied Mathematics and Computation, vol. 199, no. 1, pp. 186–194, 2008. View at Publisher · View at Google Scholar · View at Scopus
  4. S. Kirkpatrick, C. D. Gelatt, and M. P. Vecchi, “Optimization by simulated annealing,” Science, vol. 200, pp. 671–680, 1983. View at Google Scholar · View at Scopus
  5. Y. Jin and J. Branke, “Evolutionary optimization in uncertain environments—a survey,” IEEE Transactions on Evolutionary Computation, vol. 9, no. 3, pp. 303–317, 2005. View at Publisher · View at Google Scholar · View at Scopus
  6. M. Dorigo and L. M. Gambardella, “Ant colony system: a cooperative learning approach to the traveling salesman problem,” IEEE Transactions on Evolutionary Computation, vol. 1, no. 1, pp. 53–66, 1997. View at Publisher · View at Google Scholar · View at Scopus
  7. Y. Fukuyama and H. Yoshida, “A particle swarm optimization for reactive power and voltage control in electric power systems,” in Proceedings of the Congress on Evolutionary Computation, pp. 87–93, May 2001. View at Scopus
  8. Y.-H. Shi and R. C. Eberhart, “Empirical study of particle swarm optimization,” in Proceedings of IEEE International Conference on Evolutionary Computation, pp. 1945–1950, Washington, DC, USA, 1999.
  9. J.-Y. Wu, “Solving unconstrained global optimization problems via hybrid swarm intelligence approaches,” Mathematical Problems in Engineering, vol. 2013, Article ID 256180, 15 pages, 2013. View at Publisher · View at Google Scholar
  10. W.-T. Pan, “A new fruit fly optimization algorithm: taking the financial distress model as an example,” Knowledge-Based Systems, vol. 26, no. 2, pp. 69–74, 2012. View at Publisher · View at Google Scholar · View at Scopus
  11. H.-Z. Li, S. Guo, C.-J. Li, and J.-Q. Sun, “A hybrid annual power load forecasting model based on generalized regression neural network with fruit fly optimization algorithm,” Knowledge-Based Systems, vol. 37, pp. 378–387, 2013. View at Google Scholar
  12. H.-L. Shieh, C.-C. Kuo, and C.-M. Chiang, “Modified particle swarm optimization algorithm with simulated annealing behavior and its numerical verification,” Applied Mathematics and Computation, vol. 218, no. 8, pp. 4365–4383, 2011. View at Publisher · View at Google Scholar · View at Scopus
  13. B. Liu, L. Wang, Y.-H. Jin, F. Tang, and D.-X. Huang, “Improved particle swarm optimization combined with chaos,” Chaos, Solitons and Fractals, vol. 25, no. 5, pp. 1261–1271, 2005. View at Publisher · View at Google Scholar · View at Scopus