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Mathematical Problems in Engineering
Volume 2012 (2012), Article ID 841410, 36 pages
Solving Constrained Global Optimization Problems by Using Hybrid Evolutionary Computing and Artificial Life Approaches
Department of Business Administration, Lunghwa University of Science and Technology, No. 300, Section 1, Wanshou Road, Guishan, Taoyuan County 333, Taiwan
Received 28 February 2012; Revised 15 April 2012; Accepted 19 April 2012
Academic Editor: Jung-Fa Tsai
Copyright © 2012 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.
- C. A. Floudas, P. M. Pardalos, C. S. Adjiman, and W. R. Esposito, Handbook of Test Problems in Local and Global Optimization, Kluwer, Boston, Mass, USA, 1999.
- L. Kit-Nam Francis, “A generalized geometric-programming solution to economic production quantity model with flexibility and reliability considerations,” European Journal of Operational Research, vol. 176, no. 1, pp. 240–251, 2007.
- J. F. Tsai, “Treating free variables in generalized geometric programming problems,” Computers & Chemical Engineering, vol. 33, no. 1, pp. 239–243, 2009.
- P. Xu, “A hybrid global optimization method: the multi-dimensional case,” Journal of Computational and Applied Mathematics, vol. 155, no. 2, pp. 423–446, 2003.
- C. A. Floudas, Deterministic Global Optimization, Kluwer Academic, Boston, Mass, USA, 1999.
- C. I. Sun, J. C. Zeng, J. S. Pan, et al., “An improved vector particle swarm optimization for constrained optimization problems,” Information Sciences, vol. 181, no. 6, pp. 1153–1163, 2011.
- I. G. Tsoulos, “Solving constrained optimization problems using a novel genetic algorithm,” Applied Mathematics and Computation, vol. 208, no. 1, pp. 273–283, 2009.
- K. Deep and Dipti, “A self-organizing migrating genetic algorithm for constrained optimization,” Applied Mathematics and Computation, vol. 198, no. 1, pp. 237–250, 2008.
- J. Y. Wu and Y. K. Chung, “Real-coded genetic algorithm for solving generalized polynomial programming problems,” Journal of Advanced Computational Intelligence and Intelligent Informatics, vol. 11, no. 4, pp. 358–364, 2007.
- 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. A. Zadeh, “Fuzzy logic, neural networks, and soft computing,” Communications of the ACM, vol. 37, no. 3, pp. 77–84, 1994.
- A. Konar, Computational Intelligence-Principles, Techniques and Applications, Springer, New York, NY, USA, 2005.
- 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.
- W. F. Abd-El-Wahed, A. A. Mousa, and M. A. El-Shorbagy, “Integrating particle swarm optimization with genetic algorithms for solving nonlinear optimization problems,” Journal of Computational and Applied Mathematics, vol. 235, no. 5, pp. 1446–1453, 2011.
- R. J. Kuo and Y. S. Han, “A hybrid of genetic algorithm and particle swarm optimization for solving bi-level linear programming problem—a case study on supply chain model,” Applied Mathematical Modelling, vol. 35, no. 8, pp. 3905–3917, 2011.
- 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.
- Y. Hu, Y. Ding, and K. Hao, “An immune cooperative particle swarm optimization algorithm for fault-tolerant routing optimization in heterogeneous wireless sensor networks,” Mathematical Problems in Engineering, vol. 2012, Article ID 743728, 19 pages, 2012.
- X. Zhao, “A perturbed particle swarm algorithm for numerical optimization,” Applied Soft Computing, vol. 10, no. 1, pp. 119–124, 2010.
- Z. Michalewicz, Genetic Algorithms + Data Structures = Evolution Programs, Springer, New York, NY, USA, 1994.
- Z. Michalewicz, “Genetic algorithm, numerical optimization, and constraints,” in Proceedings of the 6th International Conference on Genetic Algorithms, pp. 151–158, San Mateo, Calif, USA, 1995.
- 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.
- N. Cruz-Cortés, D. Trejo-Pérez, and C. A. Coello Coello, “Handling constraints in global optimization using an artificial immune system,” in Proceedings of the 4th International Conference on Artificial Immune Systems, pp. 234–247, Banff, Canada,, 2005.
- F.-Z. Huang, L. Wang, and Q. He, “An effective co-evolutionary differential evolution for constrained optimization,” Applied Mathematics and Computation, vol. 186, no. 1, pp. 340–356, 2007.
- E. Zahara and Y. T. Kao, “Hybrid Nelder-Mead simplex search and particle swarm optimization for constrained engineering design problems,” Expert Systems with Applications, vol. 36, no. 2, part 2, pp. 3880–3886, 2009.
- C. R. Houck, J. A. Joines, and M. G. Kay, “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, Australia, 1995.
- Y. Shi and R. Eberhart, “A modified particle swarm optimizer,” in Proceedings of the IEEE International Conference on Evolutionary Computation, pp. 69–73, Anchorage, Alaska, USA, 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.
- N. K. Jerne, “Idiotypic networks and other preconceived ideas,” Immunological Reviews, vol. 79, pp. 5–24, 1984.
- L. N. de Castro and F. J. Von Zuben, “Artificial Immune Systems: Part I—Basic Theory and Applications,” FEEC/Univ. Campinas, Campinas, Brazil, 1999, ftp://ftp.dca.fee.unicamp.br/pub/docs/vonzuben/tr_dca/trdca0199.pdf.
- 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.
- M. J. Rijckaert and X. M. Martens, “Comparison of generalized geometric programming algorithms,” Journal of Optimization Theory and Applications, vol. 26, no. 2, pp. 205–242, 1978.
- D. H. Wolpert and W. G. Macready, “No free lunch theorems for optimization,” IEEE Transactions on Evolutionary Computation, vol. 1, no. 1, pp. 67–82, 1997.