A survey on metaheuristics for stochastic combinatorial optimization

By Leonora Bianchi, Marco Dorigo, Luca Maria Gambardella, Walter J. Gutjahr. Natural Computing, 2009

765 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Industrial and Manufacturing Engineering

View Paper: https://www.semanticscholar.org/paper/11cb88c4accc796dd6be0e3b89d3a5705d2fa6f4

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Citations in the corpus

A survey on optimization metaheuristics (2013, methodology): “EAs have been widely applied with a good measure of success to combinatori al optimization problems [33,26], constrained optimization problems [55], Data Mining and Knowledge Discovery [97], etc.”
Computational optimization techniques applied to microgrids planning: A review (2015, background): “Wright [17] define different categories such as: continue and discrete, constrained and unconstrained, global and local, stochastic [18] and deterministic, multimodal and multiobjective and heuristic and metaheuristic optimization [19].”
The Ant Lion Optimizer (2015, neutral): “Such algorithms mostly beneﬁt from stochastic operators [4] that make them distinct from deterministic approaches.”
A review of simheuristics: Extending metaheuristics to deal with stochastic combinatorial optimization problems (2015, background): “[4] presented earlier examples, Glover et al.”
A survey on dynamic and stochastic vehicle routing problems (2016, background): “The uncertainty in problem descriptions can be captured in various ways, mainly distinguishing between different formalisation variants, as discussed in Bianchi et al. (2009).”
A simheuristic for routing electric vehicles with limited driving ranges and stochastic travel times (2019, background): “Stochastic combinatorial optimization has received increasing interest during the last decades (Bianchi et al., 2009, Ritzinger, Puchinger and Hartl, 2015).”
Reinforcement Learning for Primary care e Appointment Scheduling (2017, background): “Implementations considering metaheuristic approaches Metaheuristics are high-level general heuristics inspired in natural phenomena, developed to find heuristics that may provide sufficiently good solutions to optimization problems, particularly in settings of limited resources or information [25].”
A Review of Random Search Methods (2015, neutral): “[20].”
Feature selection for high-dimensional classification using a competitive swarm optimizer (2016, background): “…solving complex combinatorial optimization problems, various metaheuristics have been proposed, such as genetic algorithms (GAs) (Han and Kim 2002), differential evolution (DE) (Price et al. 2006), and particle swarm optimization (PSO) (Banks et al. 2008), among many others (Bianchi et al. 2009).”