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Global Multi-objective Optimization by Means of Cell Mapping Techniques

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EVOLVE – A Bridge between Probability, Set Oriented Numerics and Evolutionary Computation VII

Part of the book series: Studies in Computational Intelligence ((SCI,volume 662))

Abstract

Multi-objective optimization problems (MOPs) arise in many fields in engineering. In this chapter we argue that adaptation of cell mapping techniques, originally designed for the global analysis of dynamical systems, are well-suited for the thorough analysis of low-dimensional MOPs. Algorithms of this kind deliver an approximation of the set of global solutions, the Pareto set, as well as the set of locally optimal and nearly optimal solutions in one run of the algorithm which may significantly improve the underlying decision making process. We underline the statements on some illustrative examples and present comparisons to other algorithms.

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Authors and Affiliations

  1. Computer Science Department, CINVESTAV-IPN, Mexico City, Mexico

    Carlos Hernández & Oliver Schütze

  2. School of Engineering, University of California Merced, Merced, USA

    Jian-Qiao Sun

Authors
  1. Carlos Hernández
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  2. Oliver Schütze
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  3. Jian-Qiao Sun
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Corresponding author

Correspondence to Carlos Hernández .

Editor information

Editors and Affiliations

  1. Leiden Institute of Advanced Com. Sci., Leiden University Leiden Institute of Advanced Com. Sci., CA Leiden, The Netherlands

    Michael Emmerich

  2. Leiden Institute of Advanced Com. Sci., Leiden University Leiden Institute of Advanced Com. Sci., CA Leiden, The Netherlands

    André Deutz

  3. Depto. de Ingeniería Eléctrica, CINVESTAV-IPN Depto. de Ingeniería Eléctrica, México, D.F., Mexico

    Oliver Schütze

  4. University of Bordeaux, Université Bordeaux Victoire , Bordeaux Cedex, France

    Pierrick Legrand

  5. Luxembourg Centre for Systems Biomedicin, University of Luxembourg Luxembourg Centre for Systems Biomedicin, Belval, Luxembourg

    Emilia Tantar

  6. Computer Science & Comm. Research Unit, University of Luxembourg Computer Science & Comm. Research Unit, Luxembourg, Luxembourg

    Alexandru-Adrian Tantar

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Hernández, C., Schütze, O., Sun, JQ. (2017). Global Multi-objective Optimization by Means of Cell Mapping Techniques. In: Emmerich, M., Deutz, A., Schütze, O., Legrand, P., Tantar, E., Tantar, AA. (eds) EVOLVE – A Bridge between Probability, Set Oriented Numerics and Evolutionary Computation VII. Studies in Computational Intelligence, vol 662. Springer, Cham. https://doi.org/10.1007/978-3-319-49325-1_2

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  • DOI: https://doi.org/10.1007/978-3-319-49325-1_2

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