Handbook of Metaheuristics

By Michel Gendreau, Jean-Yves Potvin. 2010

1,750 citations recorded by the source corpus.

Subfields: Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/507c78dc1bb6429248e2b28962b94115cc428d45

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In Control and Systems Engineering

Citations in the corpus

An orchestrated survey of methodologies for automated software test case generation (2013, methodology): “These techniques use meta-heuristic search methods (Glover and Kochenberger , 2003) to solve such search problems.”
jMetal: A Java framework for multi-objective optimization (2011, methodology): “Like in mono-objective optimization, metaheuristics [2,3] are approximated algorithms for solving MOPs.”
Heuristics for multi-attribute vehicle routing problems: A survey and synthesis (2013, background): “Although much effort has been recently put into properly defining the scope of hybrid metaheuristics (Raidl et al. 2010, Blum et al. 2011), the term remains very general and covers very different strategies.”
On the Min-cost Traveling Salesman Problem with Drone (2015, methodology): “waiting time and endurance is correctly handled. (E) 4. A Greedy Randomized Adaptive Search Procedure (GRASP) for TSP-D 305 This section presents a Greedy Randomized Adaptive Search Procedure (GRASP) [22] to solve the min-cost TSP-D. We also adapt our split procedure to solve the min-time TSP-D. In ”
Discrete cuckoo search algorithm for the travelling salesman problem (2014, background): “The need to quickly find good (not necessarily optimal) solutions to these problems has led to the development of various approximation algorithms such as metaheuristics [3, 14].”
Swarm Intelligence in Optimization (2008, background): “For books and surveys on metaheuristics see [144, 68, 18, 80].”
Thirty Years of Inventory Routing (2014, methodology): “They rely on the concept of metaheuristics, which apply local search procedures and a strategy to avoid local optima, and perform a thorough evaluation of the search space (Gendreau and Potvin 2010).”
Machine Learning for Combinatorial Optimization: a Methodological Tour d'Horizon (2018, background): “The interested reader is referred to Fischetti and Lodi (2011) and Gendreau and Potvin (2010).”
The jMetal framework for multi-objective optimization: Design and architecture (2010, neutral): “Among them, Metaheuristics [1] are widely used.”
Artificial intelligence in supply chain management: theory and applications (2010, background): “…as a general algorithmic framework that can be applied to a wide set of different combinatorial optimisation problems with relatively few modiﬁcations to make them adapted to a speciﬁc transportation network design problem (see e.g., Glover and Kochenberger 2003 for details of meta-heuristics).”