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Computational Intelligence and Neuroscience
Volume 2014 (2014), Article ID 970456, 9 pages
http://dx.doi.org/10.1155/2014/970456
Research Article

An Improved Hybrid Encoding Cuckoo Search Algorithm for 0-1 Knapsack Problems

1School of Information Engineering, Shijiazhuang University of Economics, Shijiazhuang 050031, China
2School of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, China

Received 31 August 2013; Revised 4 December 2013; Accepted 16 December 2013; Published 12 January 2014

Academic Editor: Christian W. Dawson

Copyright © 2014 Yanhong Feng 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. G. B. Dantzig, “Discrete-variable extremum problems,” Operations Research, vol. 5, no. 2, pp. 266–288, 1957. View at Google Scholar
  2. L. Jourdan, M. Basseur, and E.-G. Talbi, “Hybridizing exact methods and metaheuristics: a taxonomy,” European Journal of Operational Research, vol. 199, no. 3, pp. 620–629, 2009. View at Publisher · View at Google Scholar · View at Scopus
  3. A. V. Cabot, “An enumeration algorithm for knapsack problems,” Operations Research, vol. 18, no. 2, pp. 306–311, 1970. View at Publisher · View at Google Scholar
  4. R. J. W. James and Y. Nakagawa, “Enumeration methods for repeatedly solving Multidimensional Knapsack sub-problems,” IEICE Transactions on Information and Systems, vol. 88, no. 10, pp. 2329–2340, 2005. View at Publisher · View at Google Scholar · View at Scopus
  5. P. J. Kolesar, “A branch and bound algorithm for the knapsack problem,” Management Science, vol. 13, no. 9, pp. 723–735, 1967. View at Publisher · View at Google Scholar
  6. S. Martello, Knapsack Problem: Algorithms and Computer Implementations, John Wiley & Sons, New York, NY, USA, 1990.
  7. F.-T. Lin, “Solving the knapsack problem with imprecise weight coefficients using genetic algorithms,” European Journal of Operational Research, vol. 185, no. 1, pp. 133–145, 2008. View at Publisher · View at Google Scholar · View at Scopus
  8. J. C. Bansal and K. Deep, “A modified binary particle swarm optimization for knapsack problems,” Applied Mathematics and Computation, vol. 218, no. 22, pp. 11042–11061, 2012. View at Google Scholar
  9. M. Kong, P. Tian, and Y. Kao, “A new ant colony optimization algorithm for the multidimensional Knapsack problem,” Computers and Operations Research, vol. 35, no. 8, pp. 2672–2683, 2008. View at Publisher · View at Google Scholar · View at Scopus
  10. M. H. Kashan, N. Nahavandi, and A. H. Kashan, “DisABC: a new artificial bee colony algorithm for binary optimization,” Applied Soft Computing Journal, vol. 12, no. 1, pp. 342–352, 2012. View at Publisher · View at Google Scholar · View at Scopus
  11. L. Wang, X. Fu, Y. Mao et al., “A novel modified binary differential evolution algorithm and its applications,” Neurocomputing, vol. 98, pp. 55–75, 2012. View at Google Scholar
  12. D. Zou, L. Gao, S. Li, and J. Wu, “Solving 0-1 knapsack problem by a novel global harmony search algorithm,” Applied Soft Computing Journal, vol. 11, no. 2, pp. 1556–1564, 2011. View at Publisher · View at Google Scholar · View at Scopus
  13. L. H. Guo, G. G. Wang, H. Wang et al., “An effective hybrid firefly algorithm with harmony search for global numerical optimization,” The Scientific World Journal, vol. 2013, Article ID 125625, 9 pages, 2013. View at Publisher · View at Google Scholar
  14. G. G. Wang, A. H. Gandomi, and A. H. Alavi, “An effective krill herd algorithm with migration operator in biogeography-based optimization,” Applied Mathematical Modelling, 2013. View at Publisher · View at Google Scholar
  15. G. G. Wang, A. H. Gandomi, and A. H. Alavi, “Stud krill herd algorithm,” Neurocomputing, 2013. View at Publisher · View at Google Scholar
  16. G. G. Wang, L. H. Guo, H. Wang et al., “Incorporating mutation scheme into krill herd algorithm for global numerical optimization,” Neural Computing and Applications, 2012. View at Publisher · View at Google Scholar
  17. X.-S. Yang and S. Deb, “Cuckoo search via Lévy flights,” in Proceedings of the IEEE World Congress on Nature and Biologically Inspired Computing (NABIC '09), pp. 210–214, December 2009. View at Publisher · View at Google Scholar · View at Scopus
  18. 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
  19. X. S. Yang, Nature-Inspired Metaheuristic Algorithms, Luniver Press, Frome, UK, 2nd edition, 2010.
  20. K. Chaowanawate and A. Heednacram, “Implementation of cuckoo search in RBF neural network for flood forecasting,” in Proceedings of the 4th International Conference on Computational In-telligence, Communication Systems and Networks, pp. 22–26, 2012.
  21. V. R. Chifu, C. B. Pop, I. Salomie, D. S. Suia, and A. N. Niculici, “Optimizing the semantic web service composition process using Cuckoo Search,” Intelligent Distributed Computing V, vol. 382, pp. 93–102, 2011. View at Publisher · View at Google Scholar · View at Scopus
  22. K. Choudhary and G. N. Purohit, “A new testing approach using cuckoo search to achieve multi-objective genetic algorithm,” Journal of Computing, vol. 3, no. 4, pp. 117–119, 2011. View at Google Scholar
  23. M. Dhivya, M. Sundarambal, and L. N. Anand, “Energy efficient computation of data fusion in wireless sensor networks using cuckoo based particle approach (CBPA),” International Journal of Computer Networks and Security, vol. 4, no. 4, pp. 249–255, 2011. View at Google Scholar
  24. A. Gherboudj, A. Layeb, and S. Chikhi, “Solving 0-1 knapsack problems by a discrete binary version of cuckoo search algorithm,” International Journal of Bio-Inspired Computation, vol. 4, no. 4, pp. 229–236, 2012. View at Google Scholar
  25. A. Layeb, “A novel quantum inspired cuckoo search for knapsack problems,” International Journal of Bio-Inspired Computation, vol. 3, no. 5, pp. 297–305, 2011. View at Google Scholar
  26. X. S. Yang and S. Deb, “Cuckoo search: recent advances and applications,” Neural Computing and Applications, 2013. View at Publisher · View at Google Scholar
  27. T. K. Truong, K. Li, and Y. M. Xu, “Chemical reaction optimization with greedy strategy for the 0-1 knapsack problem,” Applied Soft Computing, vol. 13, no. 4, pp. 1774–1780, 2013. View at Publisher · View at Google Scholar
  28. Y. C. He, X. Z. Wang, and Y. Z. Kou, “A binary differential evolution algorithm with hybrid encoding,” Journal of Computer Research and Development, vol. 44, no. 9, pp. 1476–1484, 2007. View at Publisher · View at Google Scholar · View at Scopus
  29. E. G. Talbi, Metaheuristics: From Design To Implementation, John Wiley & Sons, New York, NY, USA, 2009.
  30. J. Kennedy and R. C. Eberhart, “A discrete binary version of the particle swarm algorithm,” in Proceedings of the IEEE International Conference on Computational Cybernetics and Simulation, vol. 5, pp. 4104–4108, October 1997. View at Scopus
  31. J. Zhao, T. Huang, F. Pang, and Y. Liu, “Genetic algorithm based on greedy strategy in the 0-1 knapsack problem,” in Proceedings of the 3rd International Conference on Genetic and Evolutionary Computing (WGEC '09), pp. 105–107, October 2009. View at Publisher · View at Google Scholar · View at Scopus
  32. Y. C. He, K. Q. Liu, and C. J. Zhang, “Greedy genetic algorithm for solving knapsack problems and its applications,” Computer Engineering and Design, vol. 28, no. 11, pp. 2655–2657, 2007. View at Google Scholar
  33. A. P. Engelbrecht, Fundamentals of Computational Swarm Intelligence, John Wiley & Sons, Hoboken, NJ, USA, 2009.
  34. H. Kellerer, U. Pferschy, and D. Pisinger, Knapsack Problems, Springer, Berlin, Germany, 2004.