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A Cluster-Based Evolutionary Algorithm for Multi-objective Optimization

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Computational Intelligence. Theory and Applications (Fuzzy Days 2001)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2206))

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Abstract

In this paper a new evolutionary algorithm is described for multi-objective optimization. The new method handles non-linear objective functions and constraints and supports the decision-maker with an estimation of the Pareto set. This cluster-based method applies the Pareto-dominance principle. It approximates the Pareto set with the prototypes for each cluster and alternative prototypes as secondary population. The non-dominated set is continuously being up-dated: based on the Pareto ranking, the poorest clusters are regularly deleted, and the new ones are set.

The method solves the usual test problems with a satisfactory level of accuracy.

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Author information

Authors and Affiliations

  1. University of Pécs, Faculty of Business and Economics, H-7621, Pécs, Rákóczi út 80

    István Borgulya

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  1. István Borgulya
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Editor information

Editors and Affiliations

  1. Computer Science I, University of Dortmund, 44221, Dortmund, Germany

    Bernd Reusch

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© 2001 Springer-Verlag Berlin Heidelberg

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Borgulya, I. (2001). A Cluster-Based Evolutionary Algorithm for Multi-objective Optimization. In: Reusch, B. (eds) Computational Intelligence. Theory and Applications. Fuzzy Days 2001. Lecture Notes in Computer Science, vol 2206. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45493-4_38

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  • DOI: https://doi.org/10.1007/3-540-45493-4_38

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-42732-2

  • Online ISBN: 978-3-540-45493-9

  • eBook Packages: Springer Book Archive

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