Guided Local Search

Abdullah Alsheddy (), Christos Voudouris (), Edward P. K. Tsang () and Ahmad Alhindi ()
Additional contact information
Abdullah Alsheddy: Al-Imam Muhammad Ibn Saud Islamic University (IMSIU), College of Computer and Information Sciences (CCIS)
Christos Voudouris: University of Essex, Department of Computer Science
Edward P. K. Tsang: University of Essex, Department of Computer Science
Ahmad Alhindi: Umm Al-Qura University, Department of Computer Science

Chapter 10 in Handbook of Heuristics, 2018, pp 261-297 from Springer

Abstract: 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 approachpenalty-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.

Keywords: Heuristic search; Meta-heuristics; Penalty-based methods; Guided local search; Tabu search; Constraint satisfaction (search for similar items in EconPapers)
Date: 2018
References: Add references at CitEc
Citations: View citations in EconPapers (1)

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-319-07124-4_2

Ordering information: This item can be ordered from
http://www.springer.com/9783319071244

DOI: 10.1007/978-3-319-07124-4_2

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().