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A technique for improving the convergence characteristic of genetic algorithms and its application to a genetic-based load flow algorithm

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Simulated Evolution and Learning (SEAL 1996)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1285))

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Abstract

This paper is devoted to the development of a technique for the enhancement of the convergence of genetic algorithms. Based on the concept of solution acceleration, a technique is proposed and applied to a constrained-genetic-algorithm load-flow algorithm CGALF recently developed for solving the problem of evaluating the voltage profile and power flow in electric power networks. The enhanced CGALF algorithm is applied to a practical power system to illustrate the effectiveness of the developed method.

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References

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Authors and Affiliations

  1. Artificial Intelligence and Power Systems Research Group Department of Electrical and Electronic Engineering, University of Western Australia, 6907, Nedlands, Western Australia

    Kit Po Wong & An Li

Authors
  1. Kit Po Wong
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  2. An Li
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Editor information

Xin Yao Jong-Hwan Kim Takeshi Furuhashi

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© 1997 Springer-Verlag Berlin Heidelberg

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Wong, K.P., Li, A. (1997). A technique for improving the convergence characteristic of genetic algorithms and its application to a genetic-based load flow algorithm. In: Yao, X., Kim, JH., Furuhashi, T. (eds) Simulated Evolution and Learning. SEAL 1996. Lecture Notes in Computer Science, vol 1285. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0028533

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  • DOI: https://doi.org/10.1007/BFb0028533

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-63399-0

  • Online ISBN: 978-3-540-69538-7

  • eBook Packages: Springer Book Archive

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