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Adaptive and Multilevel Metaheuristics Read chapter Read first chapter

2018 | OriginalPaper | Chapter

10. Guided Local Search

Authors: Abdullah Alsheddy, Christos Voudouris, Edward P. K. Tsang, Ahmad Alhindi

Published in: Handbook of Heuristics

Publisher: Springer International Publishing

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Abstract

Guided local search (GLS) is a meta-heuristic method proposed to solve combinatorial optimization problems. It is a high-level strategy that applies an efficient penalty-based approach to interact with the local improvement procedure. This interaction creates a process capable of escaping from local optima, which improves the efficiency and robustness of the underlying local search algorithms. Fast local search (FLS) is a way of reducing the size of the neighborhood to improve the efficiency of local search. GLS can be efficiently combined with FLS in the form of guided fast local search (GFLS). This chapter describes the principles of GLS and provides guidance for implementing and using GLS, FLS, and GFLS. It also surveys GLS extensions, hybrids, and applications to optimization, including multi-objective optimization.
previous chapter Constraint-Based Local Search
next chapter Theory of Local Search
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Metadata
Title
Guided Local Search
Authors
Abdullah Alsheddy
Christos Voudouris
Edward P. K. Tsang
Ahmad Alhindi
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_2

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