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Comparison between genetic algorithms and particle swarm optimization

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Evolutionary Programming VII (EP 1998)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1447))

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Abstract

This paper compares two evolutionary computation paradigms: genetic algorithms and particle swarm optimization. The operators of each paradigm are reviewed, focusing on how each affects search behavior in the problem space. The goals of the paper are to provide additional insights into how each paradigm works, and to suggest ways in which performance might be improved by incorporating features from one paradigm into the other.

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References

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Authors and Affiliations

  1. Department of Electrical Engineering, Indiana University Purdue University Indianapolis, 723 W. Michigan St., SL 160, 46202, Indianapolis, IN

    Russell C. Eberhart & Yuhui Shi

Authors
  1. Russell C. Eberhart
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  2. Yuhui Shi
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Editor information

V. W. Porto N. Saravanan D. Waagen A. E. Eiben

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© 1998 Springer-Verlag Berlin Heidelberg

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Eberhart, R.C., Shi, Y. (1998). Comparison between genetic algorithms and particle swarm optimization. In: Porto, V.W., Saravanan, N., Waagen, D., Eiben, A.E. (eds) Evolutionary Programming VII. EP 1998. Lecture Notes in Computer Science, vol 1447. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0040812

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  • DOI: https://doi.org/10.1007/BFb0040812

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-64891-8

  • Online ISBN: 978-3-540-68515-9

  • eBook Packages: Springer Book Archive

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