About this Journal Submit a Manuscript Table of Contents
Applied Computational Intelligence and Soft Computing
Volume 2012 (2012), Article ID 652391, 13 pages
Research Article

Multiobjective Optimization of Irreversible Thermal Engine Using Mutable Smart Bee Algorithm

Department of Mechanical Engineering, Babol University of Technology, P.O. Box 484, Babol, Iran

Received 13 July 2011; Revised 6 October 2011; Accepted 14 November 2011

Academic Editor: Chuan-Kang Ting

Copyright © 2012 M. Gorji-Bandpy and A. Mozaffari. 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. C. Lyle Cummins, Internal Fire: The Internal-Combustion Engine 1673–1900, Carnot Press, Wilsonville, Ore, USA, 2000.
  2. H. S. Leff, “Thermal efficiency at maximum power output: new results for old heat engine,” American Journal of Physics, vol. 55, pp. 602–610, 1987.
  3. A. Al-Sarkhi, B. A. Akash, J. O. Jaber, M. S. Mohsen, and E. Abu-Nada, “Efficiency of miller engine at maximum power density,” International Communications in Heat and Mass Transfer, vol. 29, no. 8, pp. 1159–1167, 2002. View at Publisher · View at Google Scholar · View at Scopus
  4. P. Y. Wang and S. S. Hou, “Performance analysis and comparison of an Atkinson cycle coupled to variable temperature heat reservoirs under maximum power and maximum power density conditions,” Energy Conversion and Management, vol. 46, no. 15-16, pp. 2637–2655, 2005. View at Publisher · View at Google Scholar · View at Scopus
  5. S. S. Hou, “Comparison of performances of air standard Atkinson and Otto cycles with heat transfer considerations,” Energy Conversion and Management, vol. 48, no. 5, pp. 1683–1690, 2007. View at Publisher · View at Google Scholar · View at Scopus
  6. J. Dréo, P. Siarry, A. Pétrowski, and E. Taillard, Metaheuristics for Hard Optimization, Springer, Heidelberg, Germany, 2006.
  7. Z. Michalewicz, “Heuristic methods for evolutionary computation techniques,” Journal of Heuristics, vol. 1, no. 2, pp. 177–206, 1996. View at Scopus
  8. X. S. Yang, Nature-Inspired Metaheuristic Algorithms, Luniver Press, Beckington, UK, 2008.
  9. J. Holland, Adaptation in Natural and Artificial Systems, The University of Michigan Press, Ann Arbor, Mich, USA, 1975.
  10. L. Davis, Handbook of Genetic Algorithms, Van Nostrand Reinhold, New York, NY, USA, 1991.
  11. B. Tessema and G. G. Yen, “A self adaptive penalty function based algorithm for performing constrained optimization,” in Proceedings of the IEEE Congress on Evolutionary Computation, G. G. Yen and M. Simon, Eds., pp. 246–253, IEEE Publications, Vancouver, Canada, 2006.
  12. A. C. C. Lemonge and H. J. C. Barbosa, “An adaptive penalty scheme in genetic algorithms for constrained optimization problems,” in Proceedings of the Genetic and Evolutionary Computation Conference, G. Paun, Ed., pp. 287–294, ACM Press, New York, NY, USA, 2002.
  13. S. M. Saab, N. Kamel, T. El-Omari, and H. Owaied, “Developing optimization algorithm using artificial Bee Colony system,” Ubiquitous Computing and Communication Journal, vol. 4, pp. 391–396, 2009.
  14. M. Dorigo, V. Maniezzo, and A. Colorni, “The ant system: optimization by a colony of cooperating agents,” in Transactions on Systems Man and Cybernetics, W. Pedrycz, Ed., pp. 29–41, IEEE Publications, Alberta, Canada, 1996.
  15. J. Kennedy and R. C. Eberhart, “Particle swarm optimization,” in Proceedings of the IEEE International Conference on Neural Networks, L. M. LeCam and J. Neyman, Eds., pp. 1942–1948, IEEE Publications, Piscataway, NJ, USA, 1995.
  16. J. Kennedy, “Minds and cultures: particle swarm implications, Socially Intelligent Agents,” Ubiquitous Computing and Communication Journal, vol. 23, pp. 67–72, 1997.
  17. Y.-P. Bu, Z. Wei, and J.-S. You, “An improved PSO algorithm and its application to grid scheduling problem,” in Proceedings of the International Symposium on Computer Science and Computational Technology (ISCSCT '08), pp. 352–355, IEEE, 2008. View at Publisher · View at Google Scholar
  18. X. S. Yang and S. Deb, “Cuckoo search via Levy flights,” in Proceedings of the World Congress on Nature & Biologically Inspired Computing (NaBIC '09), F. Rothlauf, Ed., pp. 210–214, IEEE Publications, India, December 2009.
  19. S. Łukasik and S. Zak, “Firefly algorithm for continuous constrained optimization tasks,” in Proceedings of the International Conference on Computer and Computational Intelligence, N. T. Nguyen, R. Kowalczyk, and S. M. Chen, Eds., vol. 5796 of Lecture Notes in Computer Science, pp. 97–106, Springer, Wrocław, Poland, 2009. View at Publisher · View at Google Scholar
  20. X.-S. Yang, “Firefly algorithms for multimodal optimization,” in Proceedings of the Stochastic Algorithms: Foundations and Applications, vol. 5792 of Lecture Notes in Computer Science, pp. 169–178, Springer, Sapporo, Japan, 2009. View at Publisher · View at Google Scholar
  21. D. Karaboga and B. Basturk, “A powerful and efficient algorithm for numerical function optimization: artificial Bee Colony (ABC) algorithm,” Journal of Global Optimization, vol. 39, no. 3, pp. 459–471, 2007. View at Publisher · View at Google Scholar · View at Scopus
  22. 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
  23. S. Camazine and J. Sneyd, “A model of collective nectar source selection by honey bees: self-organization through simple rules,” Journal of Theoretical Biology, vol. 149, no. 4, pp. 547–571, 1991. View at Scopus
  24. D. T. Pham, A. Ghanbarzadeh, E. Koc, S. Otri, S Rahim, and M. Zaidi, “The bees algorithm,” Technical Note, Manufacturing Engineering Centre, Cardiff University, UK, 2005.
  25. T. D. Seeley, The Wisdom of the Hive: The Social Physiology of Honey Bee Colonies, Harvard University Press, 1996.
  26. Y. Yonezawa and T. Kikuchi, “Ecological algorithm for optimal ordering used by collective honey bee behavior,” in Proceedings of the 7th International Symposium on Micro Machine and Human Science, pp. 249–256, Nagoya, Japan, 1996.
  27. T. Sato and M. Hagiwara, “Bee system: finding solution by a concentrated search,” in Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, vol. 4, pp. 3954–3959, Orlando, Fla, USA, 1997.
  28. D. Teodorovic, Bee Colony Optimization (BCO), Ministry of Science of Serbia, Belgrade, Serbia, 2001.
  29. H. A. Abbass, “Marriage in honey bee optimization (MBO): a haplometrosis polygynous swarming approach,” in Proceedings of the IEEE Conference on Evolutionary Computation (ICEC '01), vol. 1, pp. 207–214, IEEE Publications, Seoul, Korea, 2001.
  30. D. Karaboga, An Idea Based on Honey Bee Swarm for Numerical Optimization, Erciyes Universitey, Kayseri, Turkey, 2005.
  31. C. S. Chong, M. Y. H. Low, A. I. Sivakumar, and K. L. Gay, “A Bee Colony Optimization Algorithm to job shop scheduling simulation,” in Proceedings of the Winter Conference, L. F. Perrone, F. P. Wieland, J. Liu, B. G. Lawson, D. M. Nichol, and R. M. Fujimoto, Eds., pp. 1954–1961, Washington, DC, USA, 2006.
  32. N. Stanarevic, M. Tuba, and N. Bacanin, “Modified Artificial Bee Colony algorithm for constrained problems optimization,” International Journal of Mathematical Models and Methods in Applied Sciences, vol. 5, no. 3, pp. 644–651, 2011.
  33. S. E. Elmaghraby, H. Soewandi, and M. J. Yao, “Chance-constrained programming in activity networks: a critical evaluation,” European Journal of Operational Research, vol. 131, no. 2, pp. 440–458, 2001. View at Publisher · View at Google Scholar · View at Scopus
  34. F. Hillier, “Chance-constrained programming with 0-1 or bounded continuous decision variables,” Journal of Management Science, vol. 14, pp. 34–57, 1967.
  35. Y. Seppala, “Constructing sets of uniformly tighter linear approximations for a chance-constraint,” Journal of Management Science, vol. 17, pp. 736–749, 1971.
  36. Y. Seppälä and T. Orpana, “Experimental study on the efficiency and accuracy of a chance-constrained programming algorithm,” European Journal of Operational Research, vol. 16, no. 3, pp. 345–357, 1984. View at Scopus
  37. 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 Scopus
  38. D. Karaboga and B. Basturk, “Artificial Bee Colony (ABC) optimization algorithm for solving constrained optimization problems,” in Advances in Soft Computing: Foundations of Fuzzy Logic and Soft Computing, C. Ozturk, Ed., pp. 789–798, Springer, Berlin, Germany, 2007.
  39. R. Ebrahimi, “Performance of an Endoreversible Atkinson cycle with variable specific heat ratio of working fluid,” American Journal of Science, vol. 6, pp. 12–17, 2010.
  40. Y. Ge, L. Chen, and F. Sun, “Finite time thermodynamic modeling and analysis for an irreversible atkinson cycle,” Thermal Science, vol. 14, no. 4, pp. 887–896, 2010. View at Publisher · View at Google Scholar
  41. A. Al-Sarkhi, B. Akash, E. Abu-Nada, and I. Al-Hinti, “Efficiency of atkinson engine at maximum power density using temperature dependent specific heats,” Jordan Journal of Mechanical and Industrial Engineering, vol. 2, pp. 71–75, 2005.
  42. C. Lingen, Z. Wanli, and S. Fenguri, “Power, efficiency, entropy-generation rate and ecological optimization for a class of generalized irreversible universal heat-engine cycles,” Applied Energy, vol. 84, no. 5, pp. 512–525, 2007. View at Publisher · View at Google Scholar · View at Scopus
  43. J. Bae J, P. Y. Won, J. J. Rin, and K. Y. S. Lee, “An improved particle swarm optimization for nonconvex economic dispatch problems,” IEEE, vol. 25, pp. 156–166, 2010.