- About this Journal ·
- Abstracting and Indexing ·
- Aims and Scope ·
- Annual Issues ·
- Article Processing Charges ·
- Articles in Press ·
- Author Guidelines ·
- Bibliographic Information ·
- Citations to this Journal ·
- Contact Information ·
- Editorial Board ·
- Editorial Workflow ·
- Free eTOC Alerts ·
- Publication Ethics ·
- Reviewers Acknowledgment ·
- Submit a Manuscript ·
- Subscription Information ·
- Table of Contents
Mathematical Problems in Engineering
Volume 2013 (2013), Article ID 256180, 15 pages
Solving Unconstrained Global Optimization Problems via Hybrid Swarm Intelligence Approaches
Department of Business Administration, Lunghwa University of Science and Technology, No. 300, Section 1, Wanshou Road, Guishan, Taoyuan County 33306, Taiwan
Received 7 September 2012; Revised 3 December 2012; Accepted 4 December 2012
Academic Editor: Baozhen Yao
Copyright © 2013 Jui-Yu Wu. 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.
- W. Y. Yang, W. Cao, T.-S. Chung, and J. Morris, Applied Numerical Methods Using MATLAB, John Wiley & Sons, Hoboken, NJ, USA, 2005.
- C. Hamzaçebi, “Improving genetic algorithms' performance by local search for continuous function optimization,” Applied Mathematics and Computation, vol. 196, no. 1, pp. 309–317, 2008.
- C. C. Chen, “Two-layer particle swarm optimization for unconstrained optimization problems,” Applied Soft Computing Journal, vol. 11, no. 1, pp. 295–304, 2011.
- X. Zhao, “A perturbed particle swarm algorithm for numerical optimization,” Applied Soft Computing Journal, vol. 10, no. 1, pp. 119–124, 2010.
- M. D. Toksari, “Minimizing the multimodal functions with Ant Colony Optimization approach,” Expert Systems with Applications, vol. 36, no. 3, pp. 6030–6035, 2009.
- J. Kelsey and J. Timmis, “Immune inspired somatic contiguous hypermutation for function optimisation,” in Proceedings of the Genetic and Evolutionary Computation (GECCO '03), pp. 207–208, Chicago, Ill, USA, 2003.
- M. Gang, Z. Wei, and C. Xiaolin, “A novel particle swarm optimization algorithm based on particle migration,” Applied Mathematics and Computation, vol. 218, no. 11, pp. 6620–6626, 2012.
- H. Poorzahedy and O. M. Rouhani, “Hybrid meta-heuristic algorithms for solving network design problem,” European Journal of Operational Research, vol. 182, no. 2, pp. 578–596, 2007.
- Y. T. Kao and E. Zahara, “A hybrid genetic algorithm and particle swarm optimization for multimodal functions,” Applied Soft Computing Journal, vol. 8, no. 2, pp. 849–857, 2008.
- P. S. Shelokar, P. Siarry, V. K. Jayaraman, and B. D. Kulkarni, “Particle swarm and ant colony algorithms hybridized for improved continuous optimization,” Applied Mathematics and Computation, vol. 188, no. 1, pp. 129–142, 2007.
- M. R. Chen, X. Li, X. Zhang, and Y. Z. Lu, “A novel particle swarm optimizer hybridized with extremal optimization,” Applied Soft Computing Journal, vol. 10, no. 2, pp. 367–373, 2010.
- R. Thangaraj, M. Pant, A. Abraham, and P. Bouvry, “Particle swarm optimization: hybridization perspectives and experimental illustrations,” Applied Mathematics and Computation, vol. 217, no. 12, pp. 5208–5226, 2011.
- Z. H. Zhan, J. Zhang, Y. Li, and H. S. H. Chung, “Adaptive particle swarm optimization,” IEEE Transactions on Systems, Man, and Cybernetics, Part B, vol. 39, no. 6, pp. 1362–1381, 2009.
- A. Ratnaweera, S. K. Halgamuge, and H. C. Watson, “Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients,” IEEE Transactions on Evolutionary Computation, vol. 8, no. 3, pp. 240–255, 2004.
- M. Jiang, Y. P. Luo, and S. Y. Yang, “Stochastic convergence analysis and parameter selection of the standard particle swarm optimization algorithm,” Information Processing Letters, vol. 102, no. 1, pp. 8–16, 2007.
- J. Y. Wu, “Solving constrained global optimization problems by using hybrid evolutionary computing and artificial life approaches,” Mathematical Problems in Engineering, vol. 2012, Article ID 841410, 36 pages, 2012.
- Z. Michalewicz, Genetic Algorithms + Data Structures = Evolution Programs, Springer, New York, NY, USA, 1999.
- C. R. Houck, J. A. Joines, M. G. Kay, and in:, “A genetic algorithm for function optimization: a MATLAB implementation,” in NSCU-IE TR 95-09, North Carolina State University, Raleigh, NC, USA, 1995.
- J. Kennedy and R. Eberhart, “Particle swarm optimization,” in Proceedings of the IEEE International Conference on Neural Networks, pp. 1942–1948, Perth, WA, Australia, December 1995.
- Y. Shi and R. Eberhart, “Modified particle swarm optimizer,” in Proceedings of the IEEE International Conference on Evolutionary Computation (ICEC '98), pp. 69–73, Anchorage, Alaska, USA, May 1998.
- M. Clerc, “The swarm and the queen: towards a deterministic and adaptive particle swarm optimization,” in Proceedings of the IEEE Congress on Evolutionary Computation, pp. 1951–1957, Washington, DC, USA, 1999.
- 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.
- A. P. Engelbrecht, Fundamentals of Computational Swarm Intelligence, John Wiley & Sons, 2005.
- J. Y. Wu, “Solving constrained global optimization via artificial immune system,” International Journal on Artificial Intelligence Tools, vol. 20, no. 1, pp. 1–27, 2011.
- L. N. de Castro and F. J. Von Zuben, “Artificial Immune Systems—Part I—Basic Theory and Applications,” FEEC/Universidade Estadual de Campinas, Campinas, Brazil, 1999, ftp://ftp.dca.fee.unicamp.br/pub/docs/vonzuben/tr_dca/trdca0199.pdf.
- S. K. S. Fan and E. Zahara, “A hybrid simplex search and particle swarm optimization for unconstrained optimization,” European Journal of Operational Research, vol. 181, no. 2, pp. 527–548, 2007.