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Innovative Computing Methods and Their Applications to Engineering Problems pp 85–103Cite as

Dynamic Objectives Aggregation Methods in Multi-objective Evolutionary Optimization

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 357))

Abstract

Several approaches for solving multi-objective optimization problems entail a form of scalarization of the objectives. This chapter proposes a study of different dynamic objectives aggregation methods in the context of evolutionary algorithms. These methods are mainly based on both weighted sum aggregations and curvature variations. Since the incorporation of chaotic rules or behaviour in population-based optimization algorithms has been shown to possibly enhance their searching ability, this study proposes to introduce and evaluate also some chaotic rules in the dynamic weights generation process. A comparison analysis is presented on the basis of a campaign of computational experiments on a set of benchmark problems from the literature.

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Author information

Authors and Affiliations

  1. Dipartimento di Ingegneria dell’Informazione, Università degli Studi di Siena, Via Roma, 56, 53100, Siena, Italy

    G. Dellino

  2. Dipartimento di Scienze Economiche, Matematiche e Statistiche, Università degli Studi di Foggia, Largo Papa Giovanni Paolo II, 1, 71100, Foggia, Italy

    M. Fedele

  3. Dipartimento di Elettrotecnica ed Elettronica, Politecnico di Bari, Via E. Orabona, 4, 70125, Bari, Italy

    C. Meloni

Authors
  1. G. Dellino
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  2. M. Fedele
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  3. C. Meloni
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Editor information

Editors and Affiliations

  1. Department of Electronics Engineering and Telecommunications Faculty of Engineering, State University of Rio de Janeiro, Brazil

    Nadia Nedjah

  2. Católica do Paraná Centro de Ciências, Pontifícia Universidade, Rua Imaculada Conceição 1155, 80215-901, Curitiba, Brazil

    Leandro dos Santos Coelho

  3. Mechanical Engineering Graduate Program, Pontifical Catholic University of Parana, Imaculada Conceição, 1155, 80215-901, Curitiba, Brazil

    Viviana Cocco Mariani

  4. Faculdade de Engenharia, Universidade do Estado do Rio de Janeiro, Rua São Francisco Xavier 524 Sala 5022, 20559-900, Rio de Janeiro, Brazil

    Luiza de Macedo Mourelle

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Dellino, G., Fedele, M., Meloni, C. (2011). Dynamic Objectives Aggregation Methods in Multi-objective Evolutionary Optimization. In: Nedjah, N., dos Santos Coelho, L., Mariani, V.C., de Macedo Mourelle, L. (eds) Innovative Computing Methods and Their Applications to Engineering Problems. Studies in Computational Intelligence, vol 357. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20958-1_6

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  • DOI: https://doi.org/10.1007/978-3-642-20958-1_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-20957-4

  • Online ISBN: 978-3-642-20958-1

  • eBook Packages: EngineeringEngineering (R0)

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