Abstract
This paper proposes and compares two approaches to defeat the noise due the measurement errors in control system design of electric drives. The former is based on a penalized fitness and two cooperative-competitive survivor selection schemes, the latter is based on a survivor selection scheme which makes use of the tolerance interval related to the noise distribution. These approaches use adaptive rules in parameter setting to execute both the explicit and the implicit averaging in order to obtain the noise defeating in the optimization process with a relatively low number of fitness evaluations. The results show that the two approaches differently bias the population diversity and that the first can outperform the second but requires a more accurate parameter setting.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Branke, J.: Evolutionary Optimization in Dynamic Enviroments. Kluwer, Dordrecht (2001)
Jin, Y., Branke, J.: Evultionary optimization in uncertain enviroments-a survey. IEEE Trans. on Evolutionary Computation 9, 303–317 (2005)
Krishnan, R.: Electronic Motor Drives: Modeling, Analysis and Control. Prentice-Hall, Upper Saddle River (2001)
Leonhard, W.: Control of Electrical Drives, 2nd edn. Springer, Heidelberg (1996)
Caponio, A., Cascella, G.L., Neri, F., Salvatore, N., Sumner, M.: A fast adaptive memetic algorithm for on-line and off-line control design of pmsm drives. To app. IEEE Trans. on System Man and Cybernetics-B, spec. issue Memetic Alg.s (2006)
Whitley, D.: The genitor algorithm and selection pressure: Why rank-based allocation of reproductive trials is best. In: Proc. 3rd Int. Conf. on GAs, pp. 116–121 (1989)
Eshelman, L.J., Shaffer, J.D.: Real-coded genetic algorithms and interval-schemata. In: Foundations of Genetic Algorithms, vol. 2, pp. 187–202. Morgan Kaufmann, San Francisco (1993)
Ong, Y.S., Keane, A.J.: Meta-lamarkian learning in memetic algorithms. IEEE Trans. on Evolutionary Computation 8, 99–110 (2004)
NIST: e-handbook of statistical methods, http://www.itl.nist.gov/div898/handbook/
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Neri, F., Cascella, G.L., Salvatore, N., Kononova, A.V., Acciani, G. (2006). Prudent-Daring vs Tolerant Survivor Selection Schemes in Control Design of Electric Drives. In: Rothlauf, F., et al. Applications of Evolutionary Computing. EvoWorkshops 2006. Lecture Notes in Computer Science, vol 3907. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11732242_78
Download citation
DOI: https://doi.org/10.1007/11732242_78
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-33237-4
Online ISBN: 978-3-540-33238-1
eBook Packages: Computer ScienceComputer Science (R0)