Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering
Volume 2015, Article ID 126404, 17 pages
http://dx.doi.org/10.1155/2015/126404
Research Article

Multiobjective Particle Swarm Optimization Based on PAM and Uniform Design

School of Computer Science and Engineering, Guangxi Universities Key Lab of Complex System Optimization and Big Data Processing, Yulin Normal University, Yulin 537000, China

Received 26 November 2014; Accepted 3 March 2015

Academic Editor: Pandian Vasant

Copyright © 2015 Xiaoshu Zhu 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. J. D. Schaffer, “Multiple objective optimization with vector evaluated genetic algorithms,” in Proceedings of the 1st International Conference on Genetic Algorithms, pp. 93–100, 1985.
  2. L. Tang and X. Wang, “A hybrid multiobjective evolutionary algorithm for multiobjective optimization problems,” IEEE Transactions on Evolutionary Computation, vol. 17, no. 1, pp. 20–45, 2013. View at Publisher · View at Google Scholar · View at Scopus
  3. K. Deb, S. Agrawal, A. Pratap, and T. Meyarivan, “A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II,” in Parallel Problem Solving from Nature PPSN VI, vol. 1917 of Lecture Notes in Computer Science, pp. 849–858, Springer, Berlin, Germany, 2000. View at Publisher · View at Google Scholar
  4. J. Zhang, Y. Wang, and J. Feng, “Attribute index and uniform design based multiobjective association rule mining with evolutionary algorithm,” The Scientific World Journal, vol. 2013, Article ID 259347, 16 pages, 2013. View at Publisher · View at Google Scholar · View at Scopus
  5. S. Jiang and Z. Cai, “Faster convergence and higher hypervolume for multi-objective evolutionary algorithms by orthogonal and uniform design,” in Advances in Computation and Intelligence, vol. 6382 of Lecture Notes in Computer Science, pp. 312–328, Springer, Berlin, Germany, 2010. View at Publisher · View at Google Scholar
  6. J. Kennedy and R. Eberhart, “Particle swarm optimization,” in Proceedings of the IEEE International Conference on Neural Networks, pp. 1942–1948, December 1995. View at Scopus
  7. R. Eberhart and J. Kennedy, “A new optimizer using particle swarm theory,” in Proceedings of the 6th International Symposium on Micro Machine and Human Science, pp. 39–43, IEEE, Nagoya, Japan, October 1995. View at Publisher · View at Google Scholar · View at Scopus
  8. L. Yang, “A fast and elitist multi-objective particle swarm algorithm: NSPSO,” in Proceedings of the IEEE International Conference on Granular Computing (GRC '08), pp. 470–475, August 2008. View at Publisher · View at Google Scholar · View at Scopus
  9. C. A. C. Coello, G. T. Pulido, and M. S. Lechuga, “Handling multiple objectives with particle swarm optimization,” IEEE Transactions on Evolutionary Computation, vol. 8, no. 3, pp. 256–279, 2004. View at Publisher · View at Google Scholar · View at Scopus
  10. M. Clerc and J. Kennedy, “The particle swarm-explosion, stability, and convergence in a multidimensional complex space,” IEEE Transactions on Evolutionary Computation, vol. 6, no. 1, pp. 58–73, 2002. View at Publisher · View at Google Scholar · View at Scopus
  11. H. Xiaohui and R. Eberhart, “Multiobjective optimization using dynamic neighborhood particle swarm optimization,” in Proceedings of the Congress on Evolutionary Computation (CEC '02), vol. 2, pp. 1677–1681, May 2002. View at Publisher · View at Google Scholar · View at Scopus
  12. S. Mostaghim and J. Teich, “Strategies for finding good local guides in multi-objective particle swarm optimization (MOPSO),” in Proceedings of the IEEE Swarm Intelligence Symposium (SIS '03), pp. 26–33, Indianapolis, Ind, USA, April 2003. View at Publisher · View at Google Scholar
  13. T. Bartz-Beielstein, P. Limbourg, J. Mehnen, K. Schmitt, K. E. Parsopoulos, and M. N. Vrahatis, “Particle swarm optimizers for Pareto optimization with enhanced archiving techniques,” in Proceedings of the Congress on Evolutionary Computation (CEC '03), vol. 3, pp. 1780–1787, December 2003. View at Publisher · View at Google Scholar · View at Scopus
  14. K. E. Parsopoulos, D. K. Tasoulis, and M. N. Vrahatis, “Multiobjective optimization using parallel vector evaluated particle swarm optimization,” in Proceedings of the IASTED International Conference on Artificial Intelligence and Applications (AIA '04), vol. 2, pp. 823–828, February 2004. View at Scopus
  15. S.-J. Tsai, T.-Y. Sun, C.-C. Liu, S.-T. Hsieh, W.-C. Wu, and S.-Y. Chiu, “An improved multi-objective particle swarm optimizer for multi-objective problems,” Expert Systems with Applications, vol. 37, no. 8, pp. 5872–5886, 2010. View at Publisher · View at Google Scholar · View at Scopus
  16. A. A. Mousa, M. A. El-Shorbagy, and W. F. Abd-El-Wahed, “Local search based hybrid particle swarm optimization algorithm for multiobjective optimization,” Swarm and Evolutionary Computation, vol. 3, pp. 1–14, 2012. View at Publisher · View at Google Scholar · View at Scopus
  17. J. Wang, W. Liu, W. Zhang, and B. Yang, “Multi-objective particle swarm optimization based on self-update and grid strategy,” in Proceedings of the 2012 International Conference on Information Technology and Software Engineering, vol. 211 of Lecture Notes in Electrical Engineering, pp. 869–876, Springer, Berlin, Germany, 2013. View at Publisher · View at Google Scholar
  18. K. Khalili-Damghani, A.-R. Abtahi, and M. Tavana, “A new multi-objective particle swarm optimization method for solving reliability redundancy allocation problems,” Reliability Engineering & System Safety, vol. 111, pp. 58–75, 2013. View at Publisher · View at Google Scholar · View at Scopus
  19. J. Zhang, Y. Wang, and J. Feng, “Parallel particle swarm optimization based on PAM,” in Proceedings of the 2nd International Conference on Information Engineering and Computer Science (ICIECS '10), Wuhan, China, December 2010. View at Publisher · View at Google Scholar · View at Scopus
  20. J. Han and M. Kamber, Data Mining: Concepts and Techniques, Morgan Kaufmann, 2006.
  21. L. F. Ibrahim, “Using of clustering algorithm CWSP-PAM for rural network planning,” in Proceedings of the 3rd International Conference on Information Technology and Applications (ICITA '05), vol. 1, pp. 280–283, IEEE, July 2005. View at Publisher · View at Google Scholar · View at Scopus
  22. P. Chopra, J. Kang, and J. Lee, “Using gene pair combinations to improve the accuracy of the PAM classifier,” in Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine (BIBM '09), pp. 174–177, November 2009. View at Publisher · View at Google Scholar · View at Scopus
  23. Y.-W. Leung and Y. Wang, “Multiobjective programming using uniform design and genetic algorithm,” IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews, vol. 30, no. 3, pp. 293–304, 2000. View at Publisher · View at Google Scholar · View at Scopus
  24. D. Liu, K. C. Tan, C. K. Goh, and W. K. Ho, “A multiobjective memetic algorithm based on particle swarm optimization,” IEEE Transactions on Systems, Man, and Cybernetics. Part B: Cybernetics, vol. 37, no. 1, pp. 42–50, 2007. View at Publisher · View at Google Scholar · View at Scopus
  25. K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, “A fast and elitist multiobjective genetic algorithm: NSGA-II,” IEEE Transactions on Evolutionary Computation, vol. 6, no. 2, pp. 182–197, 2002. View at Publisher · View at Google Scholar · View at Scopus
  26. C. M. Fonseca and P. J. Fleming, “Multiobjective optimization and multiple constraint handling with evolutionary algorithms. II. Application example,” IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans, vol. 28, no. 1, pp. 38–47, 1998. View at Publisher · View at Google Scholar · View at Scopus
  27. F. Kursawe, “A variant of evolution strategies for vector optimization,” in Parallel Problem Solving from Nature, vol. 496 of Lecture Notes in Computer Science, pp. 193–197, Springer, Berlin, Germany, 1991. View at Publisher · View at Google Scholar
  28. E. Zitzler, K. Deb, and L. Thiele, “Comparison of multiobjective evolutionary algorithms: empirical results,” Evolutionary Computation, vol. 8, no. 2, pp. 173–195, 2000. View at Publisher · View at Google Scholar · View at Scopus
  29. H.-L. Liu, Y. Wang, and Y.-M. Cheung, “A Multi-objective evolutionary algorithm using min-max strategy and sphere coordinate transformation,” Intelligent Automation & Soft Computing, vol. 15, no. 3, pp. 361–384, 2009. View at Publisher · View at Google Scholar · View at Scopus
  30. N. Chakraborti, B. S. Kumar, V. S. Babu, S. Moitra, and A. Mukhopadhyay, “A new multi-objective genetic algorithm applied to hot-rolling process,” Applied Mathematical Modelling, vol. 32, no. 9, pp. 1781–1789, 2008. View at Publisher · View at Google Scholar · View at Scopus
  31. K. Deb, “Multi-objective genetic algorithms: problem difficulties and construction of test problems,” Evolutionary Computation, vol. 7, no. 3, pp. 205–230, 1999. View at Publisher · View at Google Scholar · View at Scopus
  32. K. Deb, L. Thiele, M. Laumanns, and E. Zitzler, “Scalable test problems for evolutionary multiobjective optimization,” in Evolutionary Multiobjective Optimization, Advanced Information and Knowledge Processing, pp. 105–145, Springer, London, UK, 2005. View at Publisher · View at Google Scholar
  33. Y. Shi and R. Eberhart, “Modified particle swarm optimizer,” in Proceedings of the IEEE World Congress on Computational Intelligence, pp. 69–73, May 1998. View at Scopus
  34. C. S. Feng, S. Cong, and X. Y. Feng, “A new adaptive inertia weight strategy in particle swarm optimization,” in Procedings of the IEEE Congress on Evolutionary Computation (CEC '07), pp. 4186–4190, IEEE, Singapore, September 2007. View at Publisher · View at Google Scholar · View at Scopus
  35. C. K. Goh, K. C. Tan, D. S. Liu, and S. C. Chiam, “A competitive and cooperative co-evolutionary approach to multi-objective particle swarm optimization algorithm design,” European Journal of Operational Research, vol. 202, no. 1, pp. 42–54, 2010. View at Publisher · View at Google Scholar · View at Scopus
  36. P. Cheng, J.-S. Pan, L. Li, Y. Tang, and C. Huang, “A survey of performance assessment for multiobjective optimizers,” in Proceedings of the 4th International Conference on Genetic and Evolutionary Computing (ICGEC '10), pp. 341–345, December 2010. View at Publisher · View at Google Scholar · View at Scopus
  37. E. Zitzler, L. Thiele, M. Laumanns, C. M. Fonseca, and V. G. Da Fonseca, “Performance assessment of multiobjective optimizers: an analysis and review,” IEEE Transactions on Evolutionary Computation, vol. 7, no. 2, pp. 117–132, 2003. View at Publisher · View at Google Scholar · View at Scopus
  38. E. Zitzler and L. Thiele, “Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach,” IEEE Transactions on Evolutionary Computation, vol. 3, no. 4, pp. 257–271, 1999. View at Publisher · View at Google Scholar · View at Scopus
  39. H. Li and Q. Zhang, “Multiobjective optimization problems with complicated pareto sets, MOEA/D and NSGA-II,” IEEE Transactions on Evolutionary Computation, vol. 13, no. 2, pp. 284–302, 2009. View at Publisher · View at Google Scholar · View at Scopus
  40. Q. Zhang, A. Zhou, S. Zhao, P. N. Suganthan, W. Liu, and S. Tiwari, “Multiobjective optimization test instances for the CEC 2009 special session and competition,” Working Report, Department of Computing and Electronic Systems, University of Essex, 2008. View at Google Scholar
  41. Q. Zhang, A. Zhou, and Y. Jin, “RM-MEDA: a regularity model-based multiobjective estimation of distribution algorithm,” IEEE Transactions on Evolutionary Computation, vol. 12, no. 1, pp. 41–63, 2008. View at Publisher · View at Google Scholar
  42. C.-S. Tsou, H.-H. Fang, H.-H. Chang, and C.-H. Kao, “An improved particle swarm Pareto optimizer with local search and clustering,” in Simulated Evolution and Learning, vol. 4247 of Lecture Notes in Computer Science, pp. 400–407, 2006. View at Publisher · View at Google Scholar
  43. U. Wickramasinghe and X. Li, “Choosing leaders for multiobjective PSO algorithms using differential evolution,” in Proceeding of the 7th International Conference Simulated Evolution and Learning, vol. 5361 of Lecture Notes in Computer Science, pp. 249–258, Springer, Berlin, Germany, 2008.
  44. T. Krink, J. S. Vesterstrom, and J. Riget, “Particle swarm optimisation with spatial particle extension,” in Proceedings of the Congress on Evolutionary Computation (CEC '02), pp. 1474–1479, May 2002. View at Publisher · View at Google Scholar · View at Scopus