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Mathematical Problems in Engineering
Volume 2013, Article ID 676275, 13 pages
http://dx.doi.org/10.1155/2013/676275
Research Article

Artificial Bee Colony Algorithm Merged with Pheromone Communication Mechanism for the 0-1 Multidimensional Knapsack Problem

College of Computer Science and Technology, Beijing University of Technology, Beijing Municipal Key Laboratory of Multimedia and Intelligent Software Technology, Beijing 100124, China

Received 24 March 2013; Accepted 11 June 2013

Academic Editor: Vishal Bhatnagar

Copyright © 2013 Junzhong Ji 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. A. Fréville, “The multidimensional 0-1 knapsack problem: an overview,” European Journal of Operational Research, vol. 155, no. 1, pp. 1–21, 2004. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet · View at Scopus
  2. A. V. Cabot, “An enumeration algorithm for knapsack problems,” Operations Research, vol. 18, pp. 306–311, 1970. View at Google Scholar
  3. W. Shih, “A branch and bound method for the multiconstraint zero-one knapsack problem,” Journal of the Operations Research Society, vol. 30, pp. 369–378, 1979. View at Google Scholar
  4. D. Bertsimas and R. Demir, “An approximate dynamic programming approach to multidimensional knapsack problems,” Management Science, vol. 48, no. 4, pp. 550–565, 2002. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  5. P. C. Chu and J. E. Beasley, “A genetic algorithm for the multidimensional knapsack problem,” Journal of Heuristics, vol. 4, no. 1, pp. 63–86, 1998. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  6. J.-C. Bai, H.-Y. Chang, and Y. Yi, “An partheno-genetic algorithm for multidimensional knapsack problem,” in Proceedings of the International Conference on Machine Learning and Cybernetics (ICMLC '05), pp. 2962–2964, August 2005. View at Scopus
  7. J. E. Gallardo, C. Cotta, and A. J. Fernández, “Solving the multidimensional knapsack problem using an evolutionary algorithm hybridized with branch and bound,” in Proceedings of the 1st International Work-Conference on the Interplay Between Natural and Artificial Computation (IWINAC '05), pp. 21–30, June 2005. View at Scopus
  8. M. Kong and P. Tian, “Application of the particle swarm optimization to the multidimensional knapsack problem, Artificial Intelligence and Soft Computing,” in Proceedings of the 8th International Conference (ICAISC '06), pp. 1140–1149, 2006.
  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 Zentralblatt MATH · View at MathSciNet · View at Scopus
  10. P. H. Rafael and D. Nikitas, “On the Performance of the Ant Colony System for Solving the Multidimensional Knapsack Problem,” in Proceedings of the IEEE Pacific Rim Conference on Communications Computers and Signal Processing (PACRIM '03), pp. 338–341, August 2003. View at Scopus
  11. I. Alaya, C. Solnon, and K. Ghedira, “Ant algorithm for the multidimensional knapsack problem,” in Proceedings of International Conference on Bioinspired Methods and their Applications, pp. 63–72, 2004.
  12. J. Z. Ji, Z. Huang, and C. N. Liu, “An ant colony optimization algorithm based on mutation and pheromone diffusion for the multidimensional knapsack problems,” Jisuanji Yanjiu yu Fazhan/Computer Research and Development, vol. 46, no. 4, pp. 644–654, 2009. View at Google Scholar · View at Scopus
  13. S. Sundar, A. Singh, and A. Rossi, “An artificial bee colony algorithm for the 0-1 multidimensional knapsack problem,” Communications in Computer and Information Science, vol. 94, no. 1, pp. 141–151, 2010. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  14. S. Pulikanti and A. Singh, “An artificial bee colony algorithm for the quadratic knapsack problem,” in Proceedings of the International Conference on Neural Information Processing (ICONIP '09), vol. 5864 of Lecture Notes in Computer Science, pp. 196–205, 2009.
  15. H. K. Wei, J. Z. Ji, Y. F. Qin, Y. M. Wang, and C. N. Liu, “A novel artificial bee colony algorithm based on attraction pheromone for the multidimensional knapsack problems, artificial,” in Proceedings of the Artificial Intelligence and Computational Intelligence (AICI '11) (part II), vol. 7003 of Lecture Notes in Computer Science, pp. 1–10, 2011.
  16. D. karaboga, “An idea based on honey bee swarm for numerical optimization,” Tech. Rep. TR06, Computer Engineering Department, Erciyes University, Kayseri, Turkey, 2005. View at Google Scholar
  17. D. Karaboga and B. Basturk, “On the performance of artificial bee colony (ABC) algorithm,” Applied Soft Computing Journal, vol. 8, no. 1, pp. 687–697, 2008. View at Publisher · View at Google Scholar · View at Scopus
  18. W.-F. Gao and S.-Y. Liu, “A modified artificial bee colony algorithm,” Computers and Operations Research, vol. 39, no. 3, pp. 687–697, 2012. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  19. X. H. Yan, Y. L. Zhu, W. P. Zou, and L. Wang, “A new approach for data clustering using hybrid artificial bee colony algorithm,” Neurocomputing, vol. 97, pp. 241–250, 2012. View at Google Scholar
  20. Q.-K. Pan, M. Fatih Tasgetiren, P. N. Suganthan, and T. J. Chua, “A discrete artificial bee colony algorithm for the lot-streaming flow shop scheduling problem,” Information Sciences, vol. 181, no. 12, pp. 2455–2468, 2011. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  21. Q. K. Pan, L. Wang, K. Mao, J. H. Zhao, and M. Zhang, “An effective artificial bee colony algorithm for a real-world hybrid flowshop problem in steelmaking process,” IEEE Transactions on Automation Science and Engineering, vol. 10, no. 2, pp. 307–322, 2013. View at Google Scholar
  22. S. N. Omkar, J. Senthilnath, R. Khandelwal, G. Narayana Naik, and S. Gopalakrishnan, “Artificial Bee Colony (ABC) for multi-objective design optimization of composite structures,” Applied Soft Computing Journal, vol. 11, no. 1, pp. 489–499, 2011. View at Publisher · View at Google Scholar · View at Scopus
  23. D. Karaboga, B. Basturk, and C. Ozturk, “Artificial bee colony (ABC) optimization algorithm for training feed-forward neural networks,” Modeling Decisions for Artificial Intelligence, vol. 4617, pp. 318–329, 2007. View at Google Scholar
  24. P. Y. Kumbhar and P. S. Krishnan, “Use of Artificial Bee Colony (ABC) algorithm in artificial neural network synthesis,” International Journal of Advanced Engineering Sciences and Technologies, vol. 11, no. 1, pp. 162–171, 2011. View at Google Scholar
  25. C. Xu and H. Duan, “Artificial bee colony (ABC) optimized edge potential function (EPF) approach to target recognition for low-altitude aircraft,” Pattern Recognition Letters, vol. 31, no. 13, pp. 1759–1772, 2010. View at Publisher · View at Google Scholar · View at Scopus
  26. B. Adil, Ö. Lale, and T. Pınar, Artificial bee colony algorithm and its application to generalized assignment problem, swarm intelligence: focus on ant and particle swarm optimization, Book edited by: F. T. S. Chan and M. K. Tiwari, Itech Education and Publishing, Vienna, Austria, 2007.
  27. P. Mukherjee and L. Satish, “Construction of equivalent circuit of a single and isolated transformer winding from FRA data using the ABC algorithm,” IEEE Transactions on Power Delivery, vol. 27, no. 2, pp. 963–970, 2012. View at Publisher · View at Google Scholar · View at Scopus
  28. N. Todorovic and S. Petrovic, “Bee colony optimization algorithm for nurse rostering,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 43, no. 2, pp. 467–473, 2013. View at Google Scholar
  29. D. Karaboga and B. Akay, “A comparative study of Artificial Bee Colony algorithm,” Applied Mathematics and Computation, vol. 214, no. 1, pp. 108–132, 2009. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet · View at Scopus
  30. A. Singh, “An artificial bee colony algorithm for the leaf-constrained minimum spanning tree problem,” Applied Soft Computing Journal, vol. 9, no. 2, pp. 625–631, 2009. View at Publisher · View at Google Scholar · View at Scopus
  31. A. Singh and A. K. Gupta, “Two heuristics for the one-dimensional bin-packing problem,” OR Spectrum, vol. 29, no. 4, pp. 765–781, 2007. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet · View at Scopus
  32. M. Dorigo, V. Maniezzo, and A. Colorni, “Ant system: optimization by a colony of cooperating agents,” IEEE Transactions on Systems, Man, and Cybernetics B, vol. 26, no. 1, pp. 29–41, 1996. View at Publisher · View at Google Scholar · View at Scopus
  33. C. Blum and M. Dorigo, “The Hyper-Cube Framework for Ant Colony Optimization,” IEEE Transactions on Systems, Man, and Cybernetics B, vol. 34, no. 2, pp. 1161–1172, 2004. View at Publisher · View at Google Scholar · View at Scopus
  34. M. F. Ali and E. D. Morgan, “Chemical communication in insect communities: a guide to insect pheromones with special emphasis on social insects,” Biological Reviews of the Cambridge Philosophical Society, vol. 65, no. 3, pp. 227–247, 1990. View at Publisher · View at Google Scholar · View at Scopus
  35. R. K. VanderMeer, M. D. Breed, K. E. Espelie, and M. L. Winston, Pheromone Communication in Social Insects, Westview Press, 1998.