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G. Raidl, J. Puchinger, C. Blum:
"Metaheuristic Hybrids";
in: "Handbook of Metaheuristics", issued by: Michel Gendreau, Jean Yves Potvin, eds.; Springer, 2019, ISBN: 978-3-319-91085-7, 385 - 417.

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 SATsolving
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.
G

http://dx.doi.org/10.1007/978-3-319-91086-4