Multiobjective memetic algorithms with quadratic approximation-based local search for expensive optimization in electromagnetics

IEEE Transactions on Magnetics

Volumn 44, Issue 6, 2008, Pages 1126-1129

  Wanner, Elizabeth F ^a   Guimarães, Frederico G ^b   Takahashi, Ricardo H C ^b   Lowther, David A ^c   Ramírez, Jaime A ^b  

^a FEDERAL UNIVERSITY OF OURO PRETO   (Brazil)

^b FEDERAL UNIVERSITY OF MINAS GERAIS   (Brazil)

^c MCGILL UNIVERSITY   (Canada)

Author keywords

Approximation techniques; Hybrid algorithms; Memetic algorithms

Indexed keywords

APPROXIMATION TECHNIQUES; HYBRID ALGORITHMS; MEMETIC ALGORITHMS; QUADRATIC APPROXIMATIONS;

APPROXIMATION THEORY; GENETIC ALGORITHMS; MULTIOBJECTIVE OPTIMIZATION; PROBLEM SOLVING; QUADRATIC PROGRAMMING;

ELECTROMAGNETIC FIELD THEORY;

EID: 44049084837     PISSN: 00189464     EISSN: None     Source Type: Journal    
DOI: 10.1109/TMAG.2007.915814     Document Type: Article

References (12)

1. P. Moscato, Memetic Algorithms: A Short Introduction in New Ideas in Optimization, D. Corne, M. Dorigo, and F. Glover, Eds. New York: McGraw-Hill, 1999, pp. 219-234.
2. Y. W. Ong and A. J. Keane, "Meta-lamarckian learning in memetic algorithms," IEEE Trans. Evol. Comput., vol. 8, no. 2, pp. 99-110, Apr. 2004.
3. J. D. Knowles, "Local-search and hybrid evolutionary algorithms for pareto optimization," Ph.D. dissertation, Dept. Comput. Sci., Univ. Reading, Reading, U.K., Jan. 2002.
4. E. F.Wanner, F. G. Guimarães, R. H. C. Takahashi, D. A. Lowther, and J. A. Ramírez, "Hybrid genetic algorithms using quadratic local search operators," Compel: The Int. J. Comput. Math. Elect. Electron. Eng., vol. 26, no. 3, pp. 773-787, 2007.
5. E. F. Wanner, F. G. Guimarães, R. H. C. Takahashi, and P. J. Fleming, "A quadratic approximation-based local search procedure for multiobjective genetic algorithms," presented at the IEEE Conf. Evol. Comput. (CEC), Vancouver, Canada, 2006.
6. S. P. Boyd, L. El Ghaoui, E. Feron, and V. Balakrishnan, "Linear matrix inequalities in system and control theory," Siam Studies in Applied Mathematics, vol. 15, 1997.
7. C. M. Fonseca and P. J. Fleming, "An overview of evolutionary algorithms in multiobjective optimization," Evol. Comput. J., vol. 3, no. 1, pp. 1-16, 1995.
8. J. F. Sturn, "Using SeDuMi 1.02, aMATLAB toolbox for optimization over symmetric cones," Opt. Methods Softw., vol. 11-12, pp. 625-653, 1999.
9. D. A. Van Veldhuizen and G. B. Lamont, "On measuring multiobjective evolutionary algorithm performance," in Proc. IEEE Conf. Evol. Comput. (CEC), 2000, pp. 204-211.
10. V. L. S. Silva, E. F. Wanner, S. A. Cerqueira, and R. H. C. Takahashi, "A new performance metric for multiobjective optimization: The integrated sphere counting," presented at the IEEE Conf. Evol. Comput. (CEC), Singapore, 2007.
11. B. Manly, Randomization and Monte Carlo Methods in Biology. London, U.K.: Chapman and Hall, 1991.
12. TEAM Workshop Problem 22: SMES Optimization Benchmark [Online]. Available: http://www-igte.tu-graz.ac.at/archive/team/index. html