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Handbook of Metaheuristics pp 469–496Cite as

Metaheuristic Hybrids

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Part of the book series: International Series in Operations Research & Management Science ((ISOR,volume 146))

Abstract

Over the last years, so-called hybrid optimization approaches have become increasingly popular for addressing hard optimization problems. In fact, when looking at leading applications of metaheuristics for complex real-world scenarios, many if not most of them do not purely adhere to one specific classical metaheuristic model but rather combine different algorithmic techniques. Concepts from different metaheuristics are often hybridized with each other, but they are also often combined with other optimization techniques such as branch-and-bound and methods from the mathematical programming and constraint programming fields. Such combinations aim at exploiting the particular advantages of the individual components, and in fact well-designed hybrids often perform substantially better than their “pure” counterparts. Many very different ways of hybridizing metaheuristics are described in the literature, and unfortunately it is usually difficult to decide which approach(es) are most appropriate in a particular situation. This chapter gives an overview of this topic by starting with a classification of metaheuristic hybrids and then discussing several prominent design templates which are illustrated by concrete examples.

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Acknowledgements

Günther R. Raidl is supported by the Austrian Science Fund (FWF) under grant 811378 and by the Austrian Exchange Service (Acciones Integradas, grant 13/2006). NICTA is funded by the Australian Government as represented by the Department of Broadband, Communications and the Digital Economy and the Australian Research Council through the ICT Centre of Excellence program. Christian Blum is supported by grants TIN2005-08818 (OPLINK) and TIN2007-66523 (FORMALISM) of the Spanish government, and by the EU project FRONTS (FP7-ICT-2007-1). He also acknowledges support from the Ramón y Cajal program of the Spanish Ministry of Science and Technology.

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  1. Institute of Computer Graphics and Algorithms, Vienna University of Technology, Vienna, Austria

    Günther R. Raidl

  2. NICTA Victoria Laboratory, University of Melbourne, Melbourne, Australia

    Jakob Puchinger

  3. ALBCOM Research Group, Universitat Politècnica de Catalunya, Barcelona, Spain

    Christian Blum

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  1. Günther R. Raidl
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Correspondence to Günther R. Raidl , Jakob Puchinger or Christian Blum .

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  1. , Département de mathématiques et génie in, École Polytechnique Montréal, Montreal, H3C 3A7, Québec, Canada

    Michel Gendreau

  2. Dépt. Informatique et Recherche, Opérationnelle, Université de Montreal, Pavillon André-Aisenstadt, Montreal, H3C 3J7, Québec, Canada

    Jean-Yves Potvin

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Raidl, G.R., Puchinger, J., Blum, C. (2010). Metaheuristic Hybrids. In: Gendreau, M., Potvin, JY. (eds) Handbook of Metaheuristics. International Series in Operations Research & Management Science, vol 146. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-1665-5_16

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