Metaheuristic Hybrids

Günther R. Raidl (), Jakob Puchinger () and Christian Blum ()
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Günther R. Raidl: Vienna University of Technology
Jakob Puchinger: University of Melbourne
Christian Blum: Universitat Politècnica de Catalunya

Chapter Chapter 16 in Handbook of Metaheuristics, 2010, pp 469-496 from Springer

Abstract: 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.

Keywords: Local Search; Tabu Search; Mixed Integer Linear Programming Problem; Column Generation; Constraint Programming (search for similar items in EconPapers)
Date: 2010
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DOI: 10.1007/978-1-4419-1665-5_16

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