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Adaptive and Multilevel Metaheuristics Read chapter Read first chapter

2018 | OriginalPaper | Chapter

24. Scatter Search

Authors : Rafael Martí, Ángel Corberán, Juanjo Peiró

Published in: Handbook of Heuristics

Publisher: Springer International Publishing

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Abstract

Scatter search (SS) is a population-based metaheuristic that has been shown to yield high-quality outcomes for hard combinatorial optimization problems. It uses strategies for combining solution vectors, making limited use of randomization, that have proved effective in a variety of problem settings. The fundamental concepts and principles were first proposed in the 1960s and 1970s as an extension of mathematical relaxation techniques for combinatorial optimization problems. Its framework is flexible, allowing the development of implementations with varying degrees of sophistication.
This chapter provides a grounding in the scatter search methodology that will allow readers to create successful applications of their own. To illustrate this, we present a scatter search implementation for a \(\mathcal {NP}\)-hard variant of the classic p-hub median problem, for which we describe search elements, mechanisms, and strategies to generate, combine, and improve solutions.

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previous chapter Random-Key Genetic Algorithms
next chapter Tabu Search
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Metadata
Title
Scatter Search
Authors
Rafael Martí
Ángel Corberán
Juanjo Peiró
Copyright Year
2018
Book
Handbook of Heuristics

Print ISBN: 978-3-319-07123-7

Electronic ISBN: 978-3-319-07124-4

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-07124-4_20

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