Table of Contents Author Guidelines Submit a Manuscript
The Scientific World Journal
Volume 2014, Article ID 637412, 19 pages
http://dx.doi.org/10.1155/2014/637412
Research Article

A Novel Harmony Search Algorithm Based on Teaching-Learning Strategies for 0-1 Knapsack Problems

School of Mathematics and Computer Science, Shaanxi University of Technology, Hanzhong 723001, China

Received 17 August 2013; Accepted 17 September 2013; Published 8 January 2014

Academic Editors: R. Alex and Z. Cui

Copyright © 2014 Shouheng Tuo 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. Z. W. Geem, J. H. Kim, and G. V. Loganathan, “A new heuristic optimization algorithm: harmony search,” Simulation, vol. 76, no. 2, pp. 60–68, 2001. View at Google Scholar · View at Scopus
  2. Z. W. Geem, “Novel derivative of harmony search algorithm for discrete design variables,” Applied Mathematics and Computation, vol. 199, no. 1, pp. 223–230, 2008. View at Publisher · View at Google Scholar · View at Scopus
  3. L. D. S. Coelho and V. C. Mariani, “An improved harmony search algorithm for power economic load dispatch,” Energy Conversion and Management, vol. 50, no. 10, pp. 2522–2526, 2009. View at Publisher · View at Google Scholar · View at Scopus
  4. A. Askarzadeh and A. Rezazadeh, “A grouping-based global harmony search algorithm for modeling of proton exchange membrane fuel cell,” International Journal of Hydrogen Energy, vol. 36, no. 8, pp. 5047–5053, 2011. View at Publisher · View at Google Scholar · View at Scopus
  5. S. Ghosh, D. Kundu, K. Suresh, S. Das, and A. Abraham, “Design of optimal digital IIR filters by using a bandwidth adaptive harmony search algorithm,” in Proceedings of the World Congress on Nature and Biologically Inspired Computing (NABIC '09), pp. 481–486, Coimbatore, India, December 2009. View at Publisher · View at Google Scholar · View at Scopus
  6. D. C. Hoang, P. Yadav, R. Kumar, and S. K. Panda, “A robust harmony search algorithm based clustering protocol for wireless sensor networks,” in Proceedings of the IEEE International Conference on Communications Workshops (ICC '10), Cape Town, South Africa, May 2010. View at Publisher · View at Google Scholar · View at Scopus
  7. M. Jaberipour and E. Khorram, “Solving the sum-of-ratios problems by a harmony search algorithm,” Journal of Computational and Applied Mathematics, vol. 234, no. 3, pp. 733–742, 2010. View at Publisher · View at Google Scholar · View at Scopus
  8. M. Poursha, F. Khoshnoudian, and A. S. Moghadam, “Harmony search based algorithms for the optimum cost design of reinforced concrete cantilever retaining walls,” International Journal of Civil Engineering, vol. 9, no. 1, pp. 1–8, 2011. View at Google Scholar
  9. A. H. Khazali and M. Kalantar, “Optimal reactive power dispatch based on harmony search algorithm,” International Journal of Electrical Power and Energy Systems, vol. 33, no. 3, pp. 684–692, 2011. View at Publisher · View at Google Scholar · View at Scopus
  10. A. H. Khazali, A. Parizad, and M. Kalantar, “Optimal voltage/reactive control by an improve harmony search algorithm,” International Review of Electrical Engineering, vol. 5, no. 1, pp. 217–224, 2010. View at Google Scholar · View at Scopus
  11. E. Khorram and M. Jaberipour, “Harmony search algorithm for solving combined heat and power economic dispatch problems,” Energy Conversion and Management, vol. 52, no. 2, pp. 1550–1554, 2011. View at Publisher · View at Google Scholar · View at Scopus
  12. D. Zou, L. Gao, J. Wu, and S. Li, “Novel global harmony search algorithm for unconstrained problems,” Neurocomputing, vol. 73, no. 16–18, pp. 3308–3318, 2010. View at Publisher · View at Google Scholar · View at Scopus
  13. Q.-K. Pan, P. N. Suganthan, M. F. Tasgetiren, and J. J. Liang, “A self-adaptive global best harmony search algorithm for continuous optimization problems,” Applied Mathematics and Computation, vol. 216, no. 3, pp. 830–848, 2010. View at Publisher · View at Google Scholar · View at Scopus
  14. Q.-K. Pan, P. N. Suganthan, J. J. Liang, and M. F. Tasgetiren, “A local-best harmony search algorithm with dynamic subpopulations,” Engineering Optimization, vol. 42, no. 2, pp. 101–117, 2010. View at Publisher · View at Google Scholar · View at Scopus
  15. S. Tuo and L. Yong, “An improved harmony search algorithm with chaos,” Journal of Computational Information Systems, vol. 8, no. 10, pp. 4269–4276, 2012. View at Google Scholar
  16. M. G. H. Omran and M. Mahdavi, “Global-best harmony search,” Applied Mathematics and Computation, vol. 198, no. 2, pp. 643–656, 2008. View at Publisher · View at Google Scholar · View at Scopus
  17. D. Zou, L. Gao, J. Wu, S. Li, and Y. Li, “A novel global harmony search algorithm for reliability problems,” Computers and Industrial Engineering, vol. 58, no. 2, pp. 307–316, 2010. View at Publisher · View at Google Scholar · View at Scopus
  18. P. Yadav, R. Kumar, S. K. Panda, and C. S. Chang, “An intelligent tuned harmony search algorithm for optimisation,” Information Sciences, 2012. View at Publisher · View at Google Scholar · View at Scopus
  19. S. Das, A. Mukhopadhyay, A. Roy, A. Abraham, and B. K. Panigrahi, “Exploratory power of the harmony search algorithm: analysis and improvements for global numerical optimization,” IEEE Transactions on Systems, Man, and Cybernetics B, vol. 41, no. 1, pp. 89–106, 2011. View at Publisher · View at Google Scholar · View at Scopus
  20. H. Sarvari and K. Zamanifar, “Improvement of harmony search algorithm by using statistical analysis,” Artificial Intelligence Review, vol. 37, no. 3, pp. 181–215, 2012. View at Publisher · View at Google Scholar · View at Scopus
  21. 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
  22. Y. Liu and C. Liu, “A schema-guiding evolutionary algorithm for 0-1 knapsack problem,” in Proceedings of the International Association of Computer Science and Information Technology—Spring Conference (IACSIT-SC '09), pp. 160–164, April 2009. View at Publisher · View at Google Scholar · View at Scopus
  23. H. Shi, “Solution to 0/1 knapsack problem based on improved ant colony algorithm,” in Proceedings of the IEEE International Conference on Information Acquisition (ICIA '06), pp. 1062–1066, August 2006. View at Publisher · View at Google Scholar · View at Scopus
  24. 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
  25. V. Boyer, D. El Baz, and M. Elkihel, “Solving knapsack problems on GPU,” Computers and Operations Research, vol. 39, no. 1, pp. 42–47, 2012. View at Publisher · View at Google Scholar · View at Scopus
  26. R. R. Hill, Y. K. Cho, and J. T. Moore, “Problem reduction heuristic for the 0-1 multidimensional knapsack problem,” Computers and Operations Research, vol. 39, no. 1, pp. 19–26, 2012. View at Publisher · View at Google Scholar · View at Scopus
  27. 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 Publisher · View at Google Scholar
  28. 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
  29. R. V. Rao, V. J. Savsani, and D. P. Vakharia, “Teaching-learning-based optimization: a novel method for constrained mechanical design optimization problems,” Computer Aided Design, vol. 43, no. 3, pp. 303–315, 2011. View at Publisher · View at Google Scholar · View at Scopus
  30. R. V. Rao, V. J. Savsani, and D. P. Vakharia, “Teaching-learning-based optimization: an optimization method for continuous non-linear large scale problems,” Information Sciences, vol. 183, no. 1, pp. 1–15, 2012. View at Publisher · View at Google Scholar · View at Scopus
  31. R. V. Rao and V. Patel, “An elitist teaching-learning-based optimization algorithm for solving complex constrained optimization problems,” International Journal of Industrial Engineering Computations, vol. 3, pp. 535–560, 2012. View at Publisher · View at Google Scholar
  32. R. V. Venkata, R. Savsani, and V. J. Rao, Mechanical Design Optimization Using Advanced Optimization Techniques, Springer, London, UK, 2012.
  33. R. V. Rao and V. Patel, “Multi-objective optimization of heat exchangers using a modified teaching-learning-based optimization algorithm,” Applied Mathematical Modelling, vol. 37, no. 3, pp. 1147–1162, 2013. View at Publisher · View at Google Scholar · View at Scopus
  34. R. Venkata Rao and V. Patel, “Multi-objective optimization of two stage thermoelectric cooler using a modified teaching-learning-based optimization algorithm,” Engineering Applications of Artificial Intelligence, vol. 26, no. 1, pp. 430–445, 2013. View at Publisher · View at Google Scholar · View at Scopus
  35. R. V. Rao, V. J. Savsani, and J. Balic, “Teaching-learning-based optimization algorithm for unconstrained and constrained real-parameter optimization problems,” Engineering Optimization, vol. 44, no. 12, pp. 1447–1462, 2012. View at Google Scholar
  36. R. V. Rao and V. Patel, “An improved teaching-learning-based optimization algorithm for solving unconstrained optimization problems,” Scientia Iranica, vol. 20, no. 3, pp. 710–720, 2013. View at Google Scholar
  37. P. J. Pawar and R. V. Rao, “Parameter optimization of machining processes using teaching-learning-based optimization algorithm,” The International Journal of Advanced Manufacturing Technology, pp. 1–12, 2012. View at Google Scholar
  38. R. Rao and V. Patel, “Comparative performance of an elitist teaching-learning-based optimization algorithm for solving unconstrained optimization problems,” International Journal of Industrial Engineering Computations, vol. 4, no. 1, 2013. View at Google Scholar
  39. S. Tuo, L. Yong, and T. Zhou, “An improved harmony search based on teaching-learning strategy for unconstrained optimization problems,” Mathematical Problems in Engineering, vol. 2013, Article ID 413565, p. 29, 2013. View at Publisher · View at Google Scholar
  40. C. A. Coello Coello, “Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: a survey of the state of the art,” Computer Methods in Applied Mechanics and Engineering, vol. 191, no. 11-12, pp. 1245–1287, 2002. View at Publisher · View at Google Scholar · View at Scopus
  41. K. Deb, “An efficient constraint handling method for genetic algorithms,” Computer Methods in Applied Mechanics and Engineering, vol. 186, no. 2–4, pp. 311–338, 2000. View at Google Scholar · View at Scopus
  42. Z. Cai and Y. Wang, “A multiobjective optimization-based evolutionary algorithm for constrained optimization,” IEEE Transactions on Evolutionary Computation, vol. 10, no. 6, pp. 658–675, 2006. View at Publisher · View at Google Scholar · View at Scopus
  43. K. Deb, “An efficient constraint handling method for genetic algorithms,” Computer Methods in Applied Mechanics and Engineering, vol. 186, no. 2–4, pp. 311–338, 2000. View at Google Scholar · View at Scopus
  44. Z. Cai and Y. Wang, “A multiobjective optimization-based evolutionary algorithm for constrained optimization,” IEEE Transactions on Evolutionary Computation, vol. 10, no. 6, pp. 658–675, 2006. View at Publisher · View at Google Scholar · View at Scopus
  45. T.-C. Chiang and Y.-P. Lai, “MOEA/D-AMS: Improving MOEA/D by an adaptive mating selection mechanism,” in Proceedings of the IEEE Congress of Evolutionary Computation (CEC '11), pp. 1473–1480, June 2011. View at Publisher · View at Google Scholar · View at Scopus
  46. N. Bacanin and M. Tuba, “Artificial bee colony (ABC) algorithm for constrained optimization improved with genetic operators,” Studies in Informatics and Control, vol. 21, no. 2, pp. 137–146, 2012. View at Google Scholar
  47. D. Bianchi, S. Genovesi, and A. Monorchio, “Constrained Pareto Optimization of Wide Band and Steerable Concentric Ring Arrays.,” IEEE Transactions on Antennas and Propagation, vol. 60, no. 7, pp. 3195–3204, 2012. View at Google Scholar
  48. P.-T. Chang and J.-H. Lee, “A fuzzy DEA and knapsack formulation integrated model for project selection,” Computers and Operations Research, vol. 39, no. 1, pp. 112–125, 2012. View at Publisher · View at Google Scholar · View at Scopus
  49. M. Daneshyari and G. G. Yen, “Constrained multiple-swarm particle swarm optimization within a cultural framework,” IEEE Transactions on Systems, Man, and Cybernetics A, vol. 42, no. 2, pp. 475–490, 2012. View at Publisher · View at Google Scholar · View at Scopus
  50. R. Datta and K. Deb, “A fast and accurate solution of constrained optimization problems using a hybrid bi-objective and penalty function approach,” in Proceedings of the IEEE Congress on Evolutionary Computation (CEC '12), Barcelona, Spain, July 2012. View at Publisher · View at Google Scholar · View at Scopus
  51. S. M. Elsayed, R. A. Sarker, and D. L. Essam, “Multi-operator based evolutionary algorithms for solving constrained optimization problems,” Computers and Operations Research, vol. 38, no. 12, pp. 1877–1896, 2011. View at Publisher · View at Google Scholar · View at Scopus
  52. A. H. Gandomi, X.-S. Yang, S. Talatahari, and S. Deb, “Coupled eagle strategy and differential evolution for unconstrained and constrained global optimization,” Computers and Mathematics with Applications, vol. 63, no. 1, pp. 191–200, 2012. View at Publisher · View at Google Scholar · View at Scopus
  53. A. W. Mohamed and H. Z. Sabry, “Constrained optimization based on modified differential evolution algorithm,” Information Sciences, vol. 194, pp. 171–208, 2012. View at Publisher · View at Google Scholar · View at Scopus