Stochastic Local Search: Foundations & Applications

By Holger H. Hoos, Thomas Stützle. 2004

1,677 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/7b58ba0c8571f36226060577e4640d05b5675e62

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Citations in the corpus

Hybrid metaheuristics in combinatorial optimization: A survey (2011, methodology): “ess sophisticated algorithms such as greedy heuristics and local earch [4].”
Ant colony optimization: Introduction and recent trends (2005, background): “For books and surveys on metaheuristics see [13,47,56,81].”
Sequence-dependent setup time flexible job shop scheduling problem to minimise total tardiness (2013, background): “Iterated local search (ILS) is a very well-known metaheuristic (Hoos and Stützle 2005).”
General k-opt submoves for the Lin–Kernighan TSP heuristic (2009, methodology): “The Lin–Kernighan algorithm [25] belongs to the class of so-called local search algorithms [17,18,20].”
Ant colony optimization theory: A survey (2005, methodology): “ACO belongs to the class of metaheuristics [8,32,40], which are approximate algorithms used to obtain good enough solutions to hard CO problems in a reasonable amount of computation time.”
The irace package: Iterated racing for automatic algorithm configuration (2016, methodology): “We are particularly interested in the tuning of metaheuristics, that is, general-purpose optimization algorithms such as evolutionary algorithms (Goldberg 1989), ant colony optimization (Dorigo and Stützle 2004), and other stochastic local search methods (Hoos and Stützle 2005).”
ParamILS: An Automatic Algorithm Configuration Framework (2014, methodology): “When we ﬁrst introduced ParamILS, we performed experiments comparing its performance against CALIBRA (Hutter et al., 2007).”
Model Composition from Interchangeable Components (2007, methodology): “Therefore we employ a global approach known as stochastic local search [ 12 ] to locate the global minimum.”
Algorithm runtime prediction: Methods & evaluation (2012, unknown): “More”
Applications of multi-objective evolutionary algorithms in economics and finance: A survey (2007, methodology): “Arma˜ nanzas and Lozano [31] apply a greedy local search algorithm [ 32 ], simulated annealing [17] and ant colony optimization (ACO) [33] to portfolio optimization problems.”
Automatically improving the anytime behaviour of optimisation algorithms (2014, background): “These plots are called performance profiles [35] or solution-quality over time (SQT) curves [19], and they can be described as a set of points representing an improvement of solution quality at a particular time.”
Iterated Local Search: Framework and Applications (2018, methodology): “classes [42, 81].”