Project Title: Genetic Algorithms for Computational Electromagnetics


In computational electromagnetics, one is often confronted with very demanding non-linear optimization problems. The optimization of the shape of an insulator in high voltage technique and the solution of inverse scattering problems are typical examples. There are also similar 'internal' optimization problems within codes for computational electromagnetics, for example, the solution of non-linear eigenvalue problems, the pole-setting procedure in the 3D MMP code and similar optimizations of the expansion of the electromagnetic field. Finite Difference (FD) and other well-known algorithms can easily be generalized by introducing weights (MEI method). This and further generalizations usually lead to the problem of an optimal selection of the parameters introduced by the generalization. It seems that Genetic Algorithms (GA) provide a powerful instrument for such tasks. It is important to recognize that GAs require a lot of experience also some commercial GA codes are available. Moreover, usual GAs are not powerful enough for most of our applications. It seems that 1) a mixture of GAs with other strategies and 2) an optimization of the parameters of the Gas are required in most cases. The latter leads to the concept of self-optimizing GAs. Also we did not yet implement such a GA, our experience is very encouraging. We were able to drastically improve the performance of eigenvalue computations and of the computation of the frequency dependence of electromagnetic phenomena by an internal optimization of the MMP code.

Contacts: Ch.Hafner, J. Froehlich, IFH, ETZG95, ETH Zentrum, CH-8092 Zurich

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Index Terms: Computational Electromagnetics, Genetic Algorithms, Optimization