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Advances in Artificial Intelligence
Volume 2012 (2012), Article ID 540861, 7 pages
http://dx.doi.org/10.1155/2012/540861
Research Article

A Novel Approach to Improve the Performance of Evolutionary Methods for Nonlinear Constrained Optimization

Center of Excellence on Soft Computing and Intelligent Information Processing (SCIIP), Ferdowsi University of Mashhad, Mashhad, Iran

Received 24 May 2012; Revised 16 July 2012; Accepted 16 July 2012

Academic Editor: Joanna Józefowska

Copyright © 2012 Alireza Rowhanimanesh and Sohrab Efati. 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. Michalewicz and M. Schoenauer, “Evolutionary algorithms for constrained parameter optimization problems,” Evolutionary Computation, vol. 4, no. 1, pp. 1–32, 1996. View at Scopus
  2. Ö. Yeniay, “Penalty function methods for constrained optimization with genetic algorithms,” Mathematical and Computational Applications, vol. 10, no. 1, pp. 45–56, 2005. View at Scopus
  3. Z. Michalewicz and G. Nazhiyath, “Genocop III: a co-evolutionary algorithm for numerical optimization problems with nonlinear constraints,” in Proceedings of the 2nd IEEE International Conference on Evolutionary Computation, pp. 647–651, December 1995. View at Scopus
  4. A. Rowhanimanesh, A. Khajekaramodin, and M.-R. Akbarzadeh-T, “Evolutionary constrained design of seismically excited buildings, actuators placement,” in Proceedings of the 1st Joint Congress on Intelligent and Fuzzy Systems (ISFS '07), pp. 297–304, Mashhad, Iran, 2007.
  5. 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
  6. M. Schouenauer and S. Xanthakis, “Constrained GA optimization,” in Proceedings of the 5th International Conference on Genetic Algorithms, pp. 473–580, 1993.
  7. A. Rowhanimanesh, A. Khajekaramodin, and M.-R. Akbarzadeh-T, “Evolutionary constrained design of seismically excited buildings: sensor placement,” Applications of Soft Computing, vol. 58, pp. 159–169, 2009.
  8. Z. Michalewicz and C. Z. Janikow, “Handling constraints in genetic algorithms,” in Proceedings of the 4th International Conference on Genetic Algorithms, pp. 151–157, 1993.
  9. M. Schoenauer and Z. Michalewicz, “Evolutionary computation at the edge of feasibility,” in Proceedings of the 4rth International Conference on Parallel Problem Solving from Nature, pp. 22–27, 1996.
  10. S. Koziel and Z. Michalewicz, “Evolutionary algorithms, homomorphous mappings, and constrained parameter optimization,” Evolutionary Computation, vol. 7, no. 1, pp. 19–44, 1999. View at Scopus
  11. H. Adeli and N. T. Cheng, “Augmented lagrangian genetic algorithm for structural optimization,” Journal of Aerospace Engineering, vol. 7, no. 1, pp. 104–118, 1994. View at Scopus
  12. B. W. Wah and Y. Chen, “Hybrid constrained simulated annealing and genetic algorithms for nonlinear constrained optimization,” in Proceedings of the Congress on Evolutionary Computation, vol. 2, pp. 925–932, May 2001. View at Scopus
  13. J. H. Kim and H. Myung, “Evolutionary programming techniques for constrained optimization problems,” IEEE Transactions on Evolutionary Computation, vol. 1, no. 2, pp. 129–140, 1997. View at Scopus
  14. T. P. Runarsson and X. Yao, “Constrained evolutionary optimization: the penalty function approach,” in Evolutionary Optimization, R. Sarker, M. Mohammadian, and X. Yao, Eds., pp. 87–113, Kluwer Academic Publishers, 2002.
  15. T. Bäck, F. Hoffmeister, and H. P. Schwell, “A survey of evolution strategies,” in Proceedings of the 4th International Conference on Genetic Algorithms, pp. 2–9, 1991.
  16. A. Homaifar, S. H. Y. Lai, and X. Qi, “Constrained optimization via genetic algorithms,” Simulation, vol. 62, no. 4, pp. 242–254, 1994. View at Scopus
  17. M. A. Kuri and C. C. Quezada, “A universal eclectic genetic algorithm for constrained optimization,” in Proceedings of the 6th European Congress on Intelligent Techniques & Soft Computing, pp. 518–522, 1998.
  18. J. A. Joines and C. R. Houck, “On the use of non-stationary penalty functions to solve nonlinear constrained optimization problems with GA's,” in Proceedings of the 1st IEEE Conference on Evolutionary Computation, pp. 579–584, June 1994. View at Scopus
  19. S. Kazarlis and V. Petridis, “Varying fitness functions in genetic algorithms: studying the rate of increase in the dynamic penalty terms,” in Proceedings of the 5th International Conference on Parallel Problem Solving from Nature, pp. 211–220, 1998.
  20. F. Mendivil and R. Shonkwiler, “Annealing a genetic algorithm for constrained optimization,” Journal of Optimization Theory and Applications, vol. 147, no. 2, pp. 395–410, 2010. View at Publisher · View at Google Scholar · View at Scopus
  21. Z. Michalewicz and N. Attia, “Evolutionary optimization of constrained problems,” in Proceedings of the 3rd Annual Conference on Evolutionary Programming, pp. 98–108, 1994.
  22. H. J. C. Barbosa and A. C. C. Lemonge, “An adaptive penalty scheme in genetic algorithms for constrained optimization problems,” in Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '02), pp. 287–294, 2002.
  23. M. Gen and R. Cheng, “A survey of penalty techniques in genetic algorithms,” in Proceedings of the IEEE International Conference on Evolutionary Computation, pp. 804–809, May 1996. View at Scopus
  24. A. Smith and D. Tate, “Genetic optimization using a penalty function,” in Proceedings of the 5th International Conference on Genetic Algorithms, pp. 499–503, 1993.
  25. R. Le Riche, C. Knopf-Lenior, and R. T. Haftka, “A segregated genetic algorithm for constrained structural optimization,” in Proceedings of the 6th International Conference on Genetic Algorithms, pp. 558–565, 1995.
  26. C. A. C. Coello, “Use of a self-adaptive penalty approach for engineering optimization problems,” Computers in Industry, vol. 41, no. 2, pp. 113–127, 2000. View at Publisher · View at Google Scholar · View at Scopus
  27. K. Deb and S. Agarwal, “A niched-penalty approach for constraint handling in genetic algorithms,” in Proceedings of the International Conference on Adaptive and Natural Computing Algorithms (ICANNGA '99), Portoroz, Slovenia, 1999.
  28. T. P. Runarsson and X. Yao, “Stochastic ranking for constrained evolutionary optimization,” IEEE Transactions on Evolutionary Computation, vol. 4, no. 3, pp. 284–294, 2000. View at Publisher · View at Google Scholar · View at Scopus
  29. 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
  30. R. L. Haupt and S. E. Haupt, Practical Genetic Algorithms, John Wiley & Sons, 2004.