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Mathematical Problems in Engineering
Volume 2014 (2014), Article ID 142194, 7 pages
http://dx.doi.org/10.1155/2014/142194
Research Article

Using the ACS Approach to Solve Continuous Mathematical Problems in Engineering

1Department of Computer Science and Engineering, National Sun Yat-sen University, 70 Lienhai Road, Kaohsiung 80424, Taiwan
2Department of Computer Science and Information Engineering, National University of Kaohsiung, 700 Kaohsiung University Road, Kaohsiung 81148, Taiwan

Received 15 February 2014; Revised 16 May 2014; Accepted 6 June 2014; Published 24 June 2014

Academic Editor: Jianquan Lu

Copyright © 2014 Min-Thai Wu 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. W.-J. Jiang, Y.-H. Xu, and Y.-S. Xu, “A novel data mining algorithm based on ant colony system,” in Proceedings of the International Conference on Machine Learning and Cybernetics (ICMLC '05), vol. 3, pp. 1919–1923, Guangzhou, China, August 2005. View at Scopus
  2. M. S. Chang and H. Y. Lin, “An immunized ant colony system algorithm to solve unequal area facility layout problems using flexible bay structure,” in Proceedings of the Institute of Industrial Engineers Asian Conference, pp. 9–17, 2013.
  3. L. M. Gambardella, R. Montemanni, and D. Weyland, “Coupling ant colony systems with strong local searches,” European Journal of Operational Research, vol. 220, no. 3, pp. 831–843, 2012. View at Publisher · View at Google Scholar · View at Scopus
  4. A. Madureira, D. Falcao, and I. Pereira, “Ant colony system based approach to single machine scheduling problems: weighted tardiness scheduling problem,” in Proceedings of the 4th World Congress on Nature and Biologically Inspired Computing (NaBIC '12), pp. 86–91, November 2012. View at Publisher · View at Google Scholar · View at Scopus
  5. O. P. Verma, P. Kumar, M. Hanmandlu, and S. Chhabra, “High dynamic range optimal fuzzy color image enhancement using artificial ant colony system,” Applied Soft Computing, vol. 12, no. 1, pp. 394–404, 2012. View at Publisher · View at Google Scholar · View at Scopus
  6. S. Yan and Y.-L. Shih, “An ant colony system-based hybrid algorithm for an emergency roadway repair time-space network flow problem,” Transportmetrica, vol. 8, no. 5, pp. 361–386, 2012. View at Publisher · View at Google Scholar · View at Scopus
  7. S. H. Pourtakdoust and H. Nobahari, “An extension of ant colony system to continuous optimization problems,” in Ant Colony Optimization and Swarm Intelligence, vol. 3172 of Lecture Notes in Computer Science, pp. 294–301, Springer, Berlin, Germany, 2004. View at Google Scholar · View at Scopus
  8. A. Karimi, H. Nobahari, and P. Siarry, “Continuous ant colony system and tabu search algorithms hybridized for global minimization of continuous multi-minima functions,” Computational Optimization and Applications, vol. 45, no. 3, Article ID MR2600899, pp. 639–661, 2010. View at Publisher · View at Google Scholar · View at Scopus
  9. L. Kuhn, Ant colony optimization for continuous space [M.S. thesis], The Department of Information Technology and Electrical Engineering of The University of Queensland, 2002.
  10. L. Hong and X. Shibo, “On ant colony algorithm for solving continuous optimization problem,” in Proceedings of the 4th International Conference on Intelligent Information Hiding and Multiedia Signal Processing (IIH-MSP '08), pp. 1450–1453, August 2008. View at Publisher · View at Google Scholar · View at Scopus
  11. N. Monmarché, G. Venturini, and M. Slimane, “On how Pachycondyla apicalis ants suggest a new search algorithm,” Future Generation Computer Systems, vol. 16, no. 8, pp. 937–946, 2000. View at Publisher · View at Google Scholar · View at Scopus
  12. K. Socha and M. Dorigo, “Ant colony optimization for continuous domains,” European Journal of Operational Research, vol. 185, no. 3, pp. 1155–1173, 2008. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  13. M. Dorigo, V. Maniezzo, and A. Colorni, “Ant system: optimization by a colony of cooperating agents,” IEEE Transactions on Systems, Man, and Cybernetics B: Cybernetics, vol. 26, no. 1, pp. 29–41, 1996. View at Publisher · View at Google Scholar · View at Scopus
  14. 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
  15. T.-P. Hong, Y.-F. Tung, S.-L. Wang, M.-T. Wu, and Y.-L. Wu, “An ACS-based framework for fuzzy data mining,” Expert Systems with Applications, vol. 36, no. 9, pp. 11844–11852, 2009. View at Publisher · View at Google Scholar · View at Scopus
  16. T.-P. Hong, Y.-F. Tung, S.-L. Wang, Y.-L. Wu, and M.-T. Wu, “A multi-level ant-colony mining algorithm for membership functions,” Information Sciences, vol. 182, no. 1, pp. 3–14, 2012. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus