Guided Local Search

Christos Voudouris (), Abdullah Alsheddy () and Ahmad Alhindi ()
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Christos Voudouris: National and Kapodistrian University of Athens, AI Team, Department of Informatics and Telecommunications
Abdullah Alsheddy: Imam Mohammad Ibn Saud Islamic University (IMSIU), College of Computer and Information Sciences (CCIS)
Ahmad Alhindi: College of Computers, Umm Al-Qura University, Department of Computer Science and Artificial Intelligence

Chapter 16 in Handbook of Heuristics, 2025, pp 427-467 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 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, especially in routing, including promising emerging approaches on combining GLS with AI.

Keywords: Heuristic search; Meta-heuristics; Penalty-based methods; Guided local search; Tabu search; Routing problems; Artificial intelligence (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/978-3-032-00385-0_2

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