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Hybrid Metaheuristics for Multi-objective Combinatorial Optimization

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Hybrid Metaheuristics

Part of the book series: Studies in Computational Intelligence ((SCI,volume 114))

Many real-world optimization problems can be modelled as combinatorial optimization problems. Often, these problems are characterized by their large size and the presence of multiple, conflicting objectives. Despite progress in solving multi-objective combinatorial optimization problems exactly, the large size often means that heuristics are required for their solution in acceptable time. Since the middle of the nineties the trend is towards heuristics that “pick and choose” elements from several of the established metaheuristic schemes. Such hybrid approximation techniques may even combine exact and heuristic approaches. In this chapter we give an overview over approximation methods in multi-objective combinatorial optimization. We briefly summarize “classical” metaheuristics and focus on recent approaches, where metaheuristics are hybridized and/or combined with exact methods.

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  1. Laboratoire d'Informatique de Nantes Atlantique, Université de Nantes, Nantes, France

    Matthias Ehrgott & Xavier Gandibleux

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  1. Matthias Ehrgott
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  1. Dept. Llenguatges i Sistemes Informátics, Universitat Politècnica de Catalunya, Jordi Girona 1-3 Omega 112, E-08034, Barcelona, Spain

    Christian Blum

  2. Dept. Llenguatges i Sistemes Informátics, Universitat Politècnica de Catalunya, Jordi Girona 1-3 Omega 213, E-08034, Barcelona, Spain

    Maria José Blesa Aguilera

  3. Università di Bologna, Via Venezia 52, I-47023, Cesena, Italy

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    Michael Sampels

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Ehrgott, M., Gandibleux, X. (2008). Hybrid Metaheuristics for Multi-objective Combinatorial Optimization. In: Blum, C., Aguilera, M.J.B., Roli, A., Sampels, M. (eds) Hybrid Metaheuristics. Studies in Computational Intelligence, vol 114. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78295-7_8

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