Abstract
The authors propose a hierarchical evolutionary algorithm (HEA) to solve structural optimization problems. The HEA is composed by a lower level evolutionary algorithm (LLEA) and a higher level evolutionary algorithm (HLEA). The HEA has been applied to the design of grounding grids for electrical safety. A compact representation to describe the topology of the grounding grid is proposed. An analysis of the decision space is carried out and its restriction is obtained according to some considerations on the physical meaning of the individuals. Due to the algorithmic structure and the specific class of problems under study, the fitness function of the HLEA is noisy. A statistical approach to analyze the behavior and the reliability of the fitness function is done by applying the limit theorems of the probability theory. The comparison with the other method of grounding grid design shows the validity and the efficiency of the HEA.
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References
IEEE Standard 80 – 2000: IEEE Guide for Safety in AC Substation Grounding (2000)
Sun, W., He, J., Gao, Y., Zeng, R., Wu, W., Su, Q.: Optimal Design Analysis of Grounding Grids for Substations built in non-uniform soil. In: Proc. of Powercon. Intern. Conference on Power System Technology, vol. 3, pp. 1455–1460 (2000)
Otero, A.F., Cidras, J., Garrido, C.: Genetic Algorithm Based Method for Grounding Grids Design. In: Proc. of the IEEE Inter. Conference on Evolutionary Computation. World Congress of Computational Intelligence, pp. 120–123 (1998)
Costa, M.C., Filho, M.L.P., Marechal, Y., Coulomb, J.-L., Cardoso, J.R.: Optimization of Grounding Grids by Response Surface and Genetic Algorithms. IEEE Transactions on Magnetics 39(3), 1301–1304 (2003)
Neri, F.: A New Evolutionary Method for Designing Grounding Grids by Touch Voltage Control. In: Proc. of IEEE Intern. Symposium on Industrial Electronics, pp. 1501–1505 (2004)
Sylos Labini, M., Covitti, A., Delvecchio, G., Marzano, C.: A Study for Optimizing the Number of Subareas in the Maxwell’s Method. IEEE Trans. on Magnetics 39(3), 1159–1162 (2003)
Eshelman, L.J.: The CHC adaptative search algorithm: How to have safe search when engaging in nontraditional genetic recombination. In: FOGA-1, pp. 265–283 (1991)
Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs, 3rd edn. Springer, Heidelberg (1996)
Goldberg, D.E., Deb, K., Clark, J.H.: Genetic algorithms, noise, and sizing of populations. Complex Systems 6(4), 333–362 (1992)
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© 2005 Springer-Verlag Berlin Heidelberg
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Neri, F., Kononova, A.V., Delvecchio, G., Labini, M.S., Uglanov, A.V. (2005). A Hierarchical Evolutionary Algorithm with Noisy Fitness in Structural Optimization Problems. In: Rothlauf, F., et al. Applications of Evolutionary Computing. EvoWorkshops 2005. Lecture Notes in Computer Science, vol 3449. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-32003-6_64
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DOI: https://doi.org/10.1007/978-3-540-32003-6_64
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