Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering
Volume 2016, Article ID 4839763, 11 pages
http://dx.doi.org/10.1155/2016/4839763
Research Article

Cuckoo Search Algorithm with Hybrid Factor Using Dimensional Distance

1College of Computer and Information Science, Fujian Agriculture and Forestry University, Fuzhou 350002, China
2College of Management, Fujian University of Traditional Chinese Medicine, Fuzhou 350002, China

Received 15 May 2016; Accepted 6 November 2016

Academic Editor: Salvatore Alfonzetti

Copyright © 2016 Yaohua Lin 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. 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, Coimbatore, India, December 2009. View at Publisher · View at Google Scholar · View at Scopus
  2. X.-S. Yang and S. Deb, “Engineering optimisation by cuckoo search,” International Journal of Mathematical Modelling and Numerical Optimisation, vol. 1, no. 4, pp. 330–343, 2010. View at Publisher · View at Google Scholar · View at Scopus
  3. F. Wang, L. G. Luo, X.-S. He, and Y. Wang, “Hybrid optimization algorithm of PSO and Cuckoo Search,” in Proceedings of the 2nd International Conference on Artificial Intelligence, Management Science and Electronic Commerce (AIMSEC '11), pp. 1172–1175, Dengfeng, China, August 2011. View at Publisher · View at Google Scholar · View at Scopus
  4. A. Ghodrati and S. Lotfi, “A hybrid CS/PSO algorithm for global optimization,” in Intelligent Information and Database Systems, vol. 7198 of Lecture Notes in Computer Science, pp. 89–98, Springer, Berlin Heidelberg, 2012. View at Google Scholar
  5. G. Wang, L. Guo, H. Duan, L. Liu, H. Wang, and J. Wang, “A hybrid meta-heuristic DE/CS Algorithm for UCAV path planning,” Journal of Information and Computational Science, vol. 9, no. 16, pp. 4811–4818, 2012. View at Google Scholar · View at Scopus
  6. R. G. Babukartik and P. Dhavachelvan, “Hybrid algorithm using the advantage of ACO and cuckoo search for job scheduling,” International Journal of Information Technology Convergence and Services, vol. 2, no. 4, pp. 25–34, 2012. View at Publisher · View at Google Scholar
  7. P. R. Srivastava, R. Khandelwal, S. Khandelwal, S. Kumar, and S. S. Ranganatha, “Automated test data generation using cuckoo search and tabu search (CSTS) algorithm,” Journal of Intelligent Systems, vol. 21, no. 2, pp. 195–224, 2012. View at Publisher · View at Google Scholar · View at Scopus
  8. X. Y. Liu and M. L. Fu, “Cuckoo search algorithm based on frog leaping local search and chaos theory,” Applied Mathematics and Computation, vol. 266, pp. 1083–1092, 2015. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  9. X. Li, J. N. Wang, and M. H. Yin, “Enhancing the performance of cuckoo search algorithm using orthogonal learning method,” Neural Computing and Applications, vol. 24, no. 6, pp. 1233–1247, 2014. View at Publisher · View at Google Scholar · View at Scopus
  10. H. Q. Zheng and Y. Q. Zhou, “A cooperative coevolutionary Cuckoo search algorithm for optimization problem,” Journal of Applied Mathematics, vol. 2013, Article ID 912056, 9 pages, 2013. View at Publisher · View at Google Scholar · View at MathSciNet
  11. X.-X. Hu and Y.-L. Yin, “Cooperative co-evolutionary cuckoo search algorithm for continuous function optimization problems,” Pattern Recognition and Artificial Intelligence, vol. 26, no. 11, pp. 1041–1049, 2013. View at Google Scholar · View at Scopus
  12. L. J. Wang, Y. W. Zhong, and Y. L. Yin, “A hybrid cooperative cuckoo search algorithm with particle swarm optimisation,” International Journal of Computing Science and Mathematics, vol. 6, no. 1, pp. 18–29, 2015. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  13. J. D. Huang, L. Gao, and X. Y. Li, “An effective teaching-learning-based cuckoo search algorithm for parameter optimization problems in structure designing and machining processes,” Applied Soft Computing Journal, vol. 36, pp. 349–356, 2015. View at Publisher · View at Google Scholar · View at Scopus
  14. S. Walton, O. Hassan, K. Morgan, and M. R. Brown, “Modified cuckoo search: a new gradient free optimisation algorithm,” Chaos, Solitons & Fractals, vol. 44, no. 9, pp. 710–718, 2011. View at Publisher · View at Google Scholar · View at Scopus
  15. T. Ljouad, A. Amine, and M. Rziza, “A hybrid mobile object tracker based on the modified Cuckoo Search algorithm and the Kalman Filter,” Pattern Recognition, vol. 47, no. 11, pp. 3597–3613, 2014. View at Publisher · View at Google Scholar · View at Scopus
  16. E. Valian, S. Mohanna, and S. Tavakoli, “Improved cuckoo search algorithm for global optimization,” International Journal of Communications and Information Technology, vol. 1, no. 1, pp. 31–44, 2011. View at Google Scholar
  17. J. Wang and B. H. Zhou, “A hybrid adaptive cuckoo search optimization algorithm for the problem of chaotic systems parameter estimation,” Neural Computing & Applications, vol. 27, no. 6, pp. 1511–1517, 2016. View at Publisher · View at Google Scholar · View at Scopus
  18. P. Mohapatra, S. Chakravarty, and P. K. Dash, “An improved cuckoo search based extreme learning machine for medical data classification,” Swarm and Evolutionary Computation, vol. 24, pp. 25–49, 2015. View at Publisher · View at Google Scholar · View at Scopus
  19. G.-G. Wang, S. Deb, A. H. Gandomi, Z. Zhang, and A. H. Alavi, “Chaotic cuckoo search,” Soft Computing, vol. 20, no. 9, pp. 3349–3362, 2015. View at Publisher · View at Google Scholar · View at Scopus
  20. L. Huang, S. Ding, S. H. Yu, J. Wang, and K. Lu, “Chaos-enhanced Cuckoo search optimization algorithms for global optimization,” Applied Mathematical Modelling, vol. 40, no. 5-6, pp. 3860–3875, 2016. View at Publisher · View at Google Scholar · View at Scopus
  21. B. Jia, B. Yu, Q. Wu, C. Wei, and R. Law, “Adaptive affinity propagation method based on improved cuckoo search,” Knowledge-Based Systems, vol. 111, pp. 27–35, 2016. View at Publisher · View at Google Scholar
  22. L. Wang, Y. Yin, and Y. Zhong, “Cuckoo search with varied scaling factor,” Frontiers of Computer Science, vol. 9, no. 4, pp. 623–635, 2015. View at Publisher · View at Google Scholar · View at Scopus
  23. L. J. Wang and Y. W. Zhong, “Cuckoo search algorithm with chaotic maps,” Mathematical Problems in Engineering, vol. 2015, Article ID 715635, 14 pages, 2015. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  24. L. D. S. Coelho, C. E. Klein, S. L. Sabat, and V. C. Mariani, “Optimal chiller loading for energy conservation using a new differential cuckoo search approach,” Energy, vol. 75, pp. 237–243, 2014. View at Publisher · View at Google Scholar · View at Scopus
  25. U. Mlakar, I. Fister Jr., and I. Fister, “Hybrid self-adaptive cuckoo search for global optimization,” Swarm and Evolutionary Computation, vol. 29, pp. 47–72, 2016. View at Publisher · View at Google Scholar
  26. X. Ding, Z. Xu, N. J. Cheung, and X. Liu, “Parameter estimation of Takagi-Sugeno fuzzy system using heterogeneous cuckoo search algorithm,” Neurocomputing, vol. 151, no. 3, pp. 1332–1342, 2015. View at Publisher · View at Google Scholar · View at Scopus
  27. L. Wang, Y. Zhong, and Y. Yin, “Nearest neighbour cuckoo search algorithm with probabilistic mutation,” Applied Soft Computing, vol. 49, pp. 498–509, 2016. View at Publisher · View at Google Scholar
  28. L. J. Wang and Y. W. Zhong, “One-position inheritance based cuckoo search algorithm,” International Journal of Computing Science and Mathematics, vol. 6, no. 6, pp. 546–554, 2015. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  29. X. T. Li and M. H. Yin, “A particle swarm inspired cuckoo search algorithm for real parameter optimization,” Soft Computing, vol. 20, no. 4, pp. 1389–1413, 2016. View at Publisher · View at Google Scholar
  30. L. J. Wang, Y. W. Zhong, and Y. L. Yin, “Orthogonal crossover cuckoo search algorithm with external archive,” Journal of Computer Research and Development, vol. 52, no. 11, pp. 2496–2507, 2015. View at Publisher · View at Google Scholar · View at Scopus
  31. L. J. Wang, Y. L. Yin, and Y. W. Zhong, “Cuckoo search algorithm with dimension by dimension improvement,” Journal of Software, vol. 24, no. 11, pp. 2687–2698, 2013. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  32. X. Li and M. Yin, “Modified cuckoo search algorithm with self adaptive parameter method,” Information Sciences, vol. 298, pp. 80–97, 2015. View at Publisher · View at Google Scholar · View at Scopus
  33. X.-S. Yang and S. Deb, “Multiobjective cuckoo search for design optimization,” Computers and Operations Research, vol. 40, no. 6, pp. 1616–1624, 2013. View at Publisher · View at Google Scholar · View at Scopus
  34. S. Hanoun, D. Creighton, and S. Nahavandi, “A hybrid cuckoo search and variable neighborhood descent for single and multiobjective scheduling problems,” The International Journal of Advanced Manufacturing Technology, vol. 75, no. 9–12, pp. 1501–1516, 2014. View at Publisher · View at Google Scholar · View at Scopus
  35. K. Chandrasekaran and S. P. Simon, “Multi-objective scheduling problem: hybrid approach using fuzzy assisted cuckoo search algorithm,” Swarm and Evolutionary Computation, vol. 5, pp. 1–16, 2012. View at Publisher · View at Google Scholar · View at Scopus
  36. X. X. Ouyang, Y. Q. Zhou, Q. F. Luo, and H. Chen, “A novel discrete cuckoo search algorithm for spherical traveling salesman problem,” Applied Mathematics & Information Sciences, vol. 7, no. 2, pp. 777–784, 2013. View at Publisher · View at Google Scholar · View at Scopus
  37. A. Ouaarab, B. Ahiod, and X.-S. Yang, “Discrete cuckoo search algorithm for the travelling salesman problem,” Neural Computing and Applications, vol. 24, no. 7-8, pp. 1659–1669, 2014. View at Publisher · View at Google Scholar · View at Scopus
  38. Y. Q. Zhou, H. Q. Zheng, Q. F. Luo, and J. Wu, “An improved cuckoo search algorithm for solving planar graph coloring problem,” Applied Mathematics and Information Sciences, vol. 7, no. 2, pp. 785–792, 2013. View at Publisher · View at Google Scholar · View at Scopus
  39. M. K. Marichelvam, T. Prabaharan, and X. S. Yang, “Improved cuckoo search algorithm for hybrid flow shop scheduling problems to minimize makespan,” Applied Soft Computing Journal, vol. 19, pp. 93–101, 2014. View at Publisher · View at Google Scholar · View at Scopus
  40. P. Dasgupta and S. Das, “A discrete inter-species cuckoo search for flowshop scheduling problems,” Computers and Operations Research, vol. 60, pp. 111–120, 2015. View at Publisher · View at Google Scholar · View at Scopus
  41. E. Teymourian, V. Kayvanfar, G. Komaki, and M. Zandieh, “Enhanced intelligent water drops and cuckoo search algorithms for solving the capacitated vehicle routing problem,” Information Sciences, vol. 334-335, pp. 354–378, 2016. View at Publisher · View at Google Scholar
  42. X. Jin, Y. Q. Liang, D. P. Tian, and F. Zhuang, “Particle swarm optimization using dimension selection methods,” Applied Mathematics and Computation, vol. 219, no. 10, pp. 5185–5197, 2013. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  43. N. Noman and H. Iba, “Accelerating differential evolution using an adaptive local search,” IEEE Transactions on Evolutionary Computation, vol. 12, no. 1, pp. 107–125, 2008. View at Publisher · View at Google Scholar · View at Scopus
  44. P. N. Suganthan, N. Hansen, J. J. Liang et al., “Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization,” KanGAL Report 2005005, Nanyang Technological University, Singapore, 2005. View at Google Scholar
  45. W. Y. Gong and Z. H. Cai, “Differential evolution with ranking-based mutation operators,” IEEE Transactions on Cybernetics, vol. 43, no. 6, pp. 2066–2081, 2013. View at Publisher · View at Google Scholar · View at Scopus
  46. S. García, D. Molina, M. Lozano, and F. Herrera, “A study on the use of non-parametric tests for analyzing the evolutionary algorithms' behaviour: a case study on the CEC'2005 Special Session on Real Parameter Optimization,” Journal of Heuristics, vol. 15, no. 6, pp. 617–644, 2009. View at Publisher · View at Google Scholar · View at Scopus