OPTIMIZATION OF ELECTROMAGNETIC DEVICES USING ARTIFICIAL IMMUNE SYSTEMS
Keywords:Artificial immune systems, electromagnetic design optimization
Optimization algorithms based on principles inspired from the immune system are capable of achieving an arbitrary set of optima, including the global solution. These algorithms differ in the way they implement the encoding, cloning, maturation and replacement steps, which are the basic ingredients of optimization algorithms based on artiï¬cial immune systems. This paper presents the Distributed Clonal Selection Algorithm (DCSA), which employs different probability distributions for the maturation step. The performance of the DCSA is compared with the Real-Coded Clonal Selection Algorithm (RCSA) and the B-Cell Algorithm (BCA) in the design of a waveguide and in the TEAMbenchmark problem 22. The DCSA presents better convergence speed, in terms of number of evaluations, being 8% faster than the RCSA and 78% faster than the BCA, for the minimization of the return loss of a 3D waveguide impedance transformer. In the 8D TEAM problem, the DCSA and RCSA respect the energy constraint with a maximum error of 2.2% while the BCA presents high violations. Regarding these methods, the DCSA achieves better values for the stray magnetic ï¬‚ux density.
L. N. de Castro and F. J. Von Zuben, Artificial Immune Systems: Part II - A Survey of Applications , Technical Report, TR - DCA 02/00, Feb. 2000.
L. N. de Castro and J. Timmis, Artificial Immune Systems: A New Computational Intelligence Approach, Berlin, Germany: Springer-Verlag, 2002.
L. N. de Castro and F. J. Von Zuben, Learning and Optimization using the Clonal Selection Principle, IEEE Trans. Evol. Comput., vol. 6, no. 3, pp. 239-251, Jun. 2002.
J. Kelsey and J. Timmis, Immune Inspired Somatic Contiguous Hypermutation for Function Optimization, Proceedings of the on Genetic and Evolutionary Computation Conference (GECCO 2003), Springer, Lecture Notes in Computer Science, vol. 2723, pp. 207-218, 2003.
F. Campelo, F. G. GuimarËœaes, H. Igarashi, and J. A. RamÂ´Ä±rez, A Clonal Selection Algorithm for Optimization in Electromagnetics, IEEE Trans. Magn., vol. 41, no. 5, pp. 1736-1739, May 2005.
L. S. Batista, F. G. GuimarËœaes and J. A. RamÂ´Ä±rez, A Distributed Clonal Selection Algorithm for Optimization in Electromagnetics, to appear in IEEE Transactions on Magnetics, vol. 45, 2009.
D. Jiao, X.-Y. Zhu, J.-M. Jin, Fast and accurate frequency-sweep calculations using asymptotic waveform evaluation and the combined-field integral equation, Radio Science, 34:5 (1999), 1055-1063.
P. Alotto, A.V. Kuntsevitch, Ch. Magele, G. Molinari, C. Paul, K. Preis, M. Repetto, K.R. Richter, Multiobjective Optimization in Magnetostatics: A Proposal for Benchmark Problems, IEEE Trans. Magn., vol. 32, no. 3, pp. 1238- 1241, May 1996. [Online]. Available: http://www.igte.tugraz.at/archive/team new/description.php.