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International Conference on Artificial Immune Systems

ICARIS 2012: Artificial Immune Systems pp 72–85Cite as

Clustering-Based Multi-objective Immune Optimization Evolutionary Algorithm

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7597))

Abstract

In everyday life, there are plentiful cases that we need to find good solutions such that risk, cost and many other factors are to be optimized. These problems are typical examples of multi-objective optimization problems. Evolutionary algorithms are often employed for solving it. Due to the characteristics of learning and adaptability, self-organization and memory capabilities, one of the biological inspired AI methods – artificial immune systems (AIS) is considered to be a class of evolutionary techniques that can be deployed for solving this problem. This paper aims to propose a new AIS-based framework focusing on distributed and self-organization characteristics. Population of solutions is decomposed into sub-populations forming clusters. Sub-populations in each cluster undergo independent evolution processes. These clusters are then combined and re-decomposed. The proposed mechanism aims to reduce the complexity in the evolution processes, enhance the exploitation ability and achieve quick convergence. It is evaluated and compared with representative algorithms.

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Author information

Authors and Affiliations

  1. Department of Industrial and Manufacturing Systems Engineering, The University of Hong Kong, Hong Kong

    Wilburn W. P. Tsang & Henry Y. K. Lau

Authors
  1. Wilburn W. P. Tsang
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  2. Henry Y. K. Lau
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Editors and Affiliations

  1. Departmento de Computación, Centro de Investigacion y de Estudios, Avanzados del Instituto Politecnico Nacional (CINVESTAV-IPN), Av. Insituto Politécnico Nacional No. 2508, Col. San Pedro Zacatenco, 07300, Mexico, D.F., Mexico

    Carlos A. Coello Coello

  2. School of Computer Science, University of Nottingham, Jubilee Campus, Wollaton Road, NG8 1BB, Nottingham, UK

    Julie Greensmith

  3. School of Computer Science, University of Nottingham, Jubilee Campus, Wollaton Road, NG81BB, Nottingham, UK

    Natalio Krasnogor

  4. Computer Laboratory, University of Cambridge, William Gates Building, 15 JJ Thomson Avenue, CB3 0FD, Cambridge, UK

    Pietro Liò

  5. Department of Mathematics and Computer Science, University of Catania, Viale A. Doria 6, 95125, Catania, Italy

    Giuseppe Nicosia  & Mario Pavone  & 

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© 2012 Springer-Verlag Berlin Heidelberg

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Tsang, W.W.P., Lau, H.Y.K. (2012). Clustering-Based Multi-objective Immune Optimization Evolutionary Algorithm. In: Coello Coello, C.A., Greensmith, J., Krasnogor, N., Liò, P., Nicosia, G., Pavone, M. (eds) Artificial Immune Systems. ICARIS 2012. Lecture Notes in Computer Science, vol 7597. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33757-4_6

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  • DOI: https://doi.org/10.1007/978-3-642-33757-4_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33756-7

  • Online ISBN: 978-3-642-33757-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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