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International Conference on Artificial Immune Systems

ICARIS 2005: Artificial Immune Systems pp 248–261Cite as

Multiobjective Optimization by a Modified Artificial Immune System Algorithm

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3627))

Abstract

The aim of this work is to propose and validate a new multiobjective optimization algorithm based on the emulation of the immune system behavior. The rationale of this work is that the artificial immune system has, in its elementary structure, the main features required by other multiobjective evolutionary algorithms described in literature. The proposed approach is compared with the NSGA2 algorithm, that is representative of the state-of-the-art in multiobjective optimization. Algorithms are tested versus three standard problems (unconstrained and constrained), and comparisons are carried out using three different metrics. Results show that the proposed approach have performances similar or better than those produced by NSGA2, and it can become a valid alternative to standard algorithms.

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Author information

Authors and Affiliations

  1. Dept. of Electrical Engineering, Politecnico di Torino, corso Duca degli Abruzzi, 24, 10129, Torino, Italy

    Fabio Freschi & Maurizio Repetto

Authors
  1. Fabio Freschi
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  2. Maurizio Repetto
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Editor information

Editors and Affiliations

  1. Evolutionary and Swarm Design Group, Dept. of Computer Science, University of Calgary, Alberta, Canada

    Christian Jacob

  2. Department of Computer Science, University of Calgary, 2500 University Drive NW, T2N 1N4, Calgary, Alberta, Canada

    Marcin L. Pilat

  3. Computer Science Department, University College London, London, UK

    Peter J. Bentley

  4. Department of Electronics, University of York, YO10 5DD, Heslington, York, UK

    Jonathan I. Timmis

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© 2005 Springer-Verlag Berlin Heidelberg

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Freschi, F., Repetto, M. (2005). Multiobjective Optimization by a Modified Artificial Immune System Algorithm. In: Jacob, C., Pilat, M.L., Bentley, P.J., Timmis, J.I. (eds) Artificial Immune Systems. ICARIS 2005. Lecture Notes in Computer Science, vol 3627. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11536444_19

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  • DOI: https://doi.org/10.1007/11536444_19

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28175-7

  • Online ISBN: 978-3-540-31875-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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