Metaheuristic Hybrids

Günther R. Raidl (), Jakob Puchinger () and Christian Blum ()
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Günther R. Raidl: Institute of Logic and Computation
Jakob Puchinger: Université Paris-Saclay
Christian Blum: Artificial Intelligence Research Institute (IIIA-CSIC)

Chapter Chapter 12 in Handbook of Metaheuristics, 2019, pp 385-417 from Springer

Abstract: Abstract Over the last decades, 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 tree-search, dynamic programming and methods from the mathematical programming, constraint programming, and SAT-solving 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 on this topic by starting with a classification of metaheuristic hybrids and then discussing several prominent design templates which are illustrated by concrete examples.

Keywords: Metaheuristics (MH); Relaxation Induced Neighborhood Search (RINS); Greedy Randomized Adaptive Search Procedure (GRASP); Generalized Minimum Spanning Tree Problem; Benders Cuts (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1007/978-3-319-91086-4_12

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