Guided Local Search

By Christos Voudouris, Edward P. K. Tsang. Handbook of Metaheuristics, 2010

413 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Industrial and Manufacturing Engineering

View Paper: https://www.semanticscholar.org/paper/6e0a00ddb630f121f6e35e4d2bbcc0c5e30f62c1

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Thomas Stützle
2. Reuven Y. Rubinstein
3. Shie Mannor
4. James Smith
5. Natalio Krasnogor
6. Patrick Siarry
7. Javad Akbari Torkestani
8. Helena Ramalhinho Lourenço

In Computer Networks and Communications

1. Thomas Stützle
2. Natalio Krasnogor
3. Shie Mannor
4. Patrick Siarry
5. Javad Akbari Torkestani
6. Helena Ramalhinho Lourenço
7. James Smith
8. Olivier C. Martin

In Industrial and Manufacturing Engineering

1. Thomas Stützle
2. Teemu Nuortio
3. Jari Kytöjoki
4. Harri Niska
5. Natalio Krasnogor
6. Shie Mannor
7. Patrick Siarry
8. Javad Akbari Torkestani

Citations in the corpus

• Evolution of Heuristics: Towards Efficient Automatic Algorithm Design Using Large Language Model (2024, methodology): “The effectiveness and efficiency of GLS depend greatly on the design of the guiding heuristics (Alsheddy et al., 2018).”
• A tutorial for competent memetic algorithms: model, taxonomy, and design issues (2005, methodology): “In [38], the local search used is based on the powerful guided local search (GLS) metaheuristic [39].”
• Iterated Local Search: Framework and Applications (2018, positive): “Balas and Vazacopoulos [4] presented a variable depth search heuristic which they called guided local search (GLS).”
• A survey on optimization metaheuristics (2013, background): “A recent survey on GLS and its applications is available in [276,275].”
• The cross entropy method for classification (2005, methodology): “Examples of this type include simulated annealing (Aarts & Korst, 1989) and guided local search (Voudouris & Tsang, 1999).”
• A cellular learning automata-based algorithm for solving the vertex coloring problem (2011, positive): “Voudouris and Tsang (2003) pro- 220 posed a Guided Local Search (GLS), which is a meta-heuristic 221 search method, to solve the combinatorial optimization problems.”
• Improved route planning and scheduling of waste collection and transport (2006, methodology): “As the name implies, the GVNT is based on three wellknown metaheuristic principles: guided local search (Voudouris & Tsang, 1998), variable neighborhood T…”
• Iterated Local Search (2001, background): “Later, they also proposed a variant of this method, SB-GLS2, which works as follows.”