By Tapani Jokinen (auth.), Prof. Marek Rudnicki, Prof. Sławomir Wiak (eds.)
From 12 to fourteen September 2002, the Academy of Humanities and Economics (AHE) hosted the workshop "Optimization and Inverse difficulties in Electromagnetism". After this bi-annual occasion, a good number of papers have been assembled and mixed during this e-book. throughout the workshop contemporary advancements and functions in optimization and inverse methodologies for electromagnetic fields have been mentioned. The contributions chosen for the current quantity conceal a large spectrum of inverse and optimum electromagnetic methodologies, starting from theoretical to sensible purposes. a few new optimum and inverse methodologies have been proposed. There are contributions on the topic of committed software.
Optimization and Inverse difficulties in Electromagnetism involves 3 thematic chapters, masking:
-General papers (survey of particular points of optimization and inverse difficulties in electromagnetism),
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Additional info for Optimization and Inverse Problems in Electromagnetism
6. The current point becomes Xn , increment the number of expioration cycles: ne =ne + 1 , and if ne ~Ne then retum to 4. 7. The best point found previousiy becomes the current point. 8. If the terrnination criterion is not verified then retum to 3. UTS algorithm is stopped if: 1. The objective function no Ionger varies significantiy, or 2. The maximum number of cycles is reached: nc =N 1• 4. GENETIC ALGORITHM GA is an iterative procedure which maintains constant the size of popuiation of candidate soiutions , .
Rahmat-Samii, E. Michielssen, Electromagnetic Optimization by Genetic Algorithms, John Willey, 1999. 8. D. R. R. Martin, "An overview of genetic algorithms, Part 1 : fundamentals", University Computing, 15, pp. 58-69, 1993. 9. S. Brisset, F. Gillon, S. Vivier, P. Brochet, "Optimization with experimental design: an approach using Taguchi's methodology and finite element simulations", IEEE Trans Magn, Vol. 37, No. 5, pp. 3530-3533, September 2001. 10. P. Alotto, B. Brandstätter, G. Fuerntratt, Ch.
315-324, lOS Press. USING QUASIRANDOM SEQUENCES IN GENETIC ALGORITHMS Heikki Maaranen, Kaisa Miettinen, and Marko M. Mäkelä University of Jyväskylä, Department of Mathematical Information Technology, FIN-40014 University of Jyväskylä, Finland Abstract: The selection of initial points in a population-based heuristic optimization method is important since it affects the search for several iterations and often has an influence on the final solution. If no a priori information about the optimization problern is available, the initial population is often selected randomly using pseudo random numbers.