Variable Neighborhood Search

By Pierre Hansen, Nenad Mladenovic. 2018

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Citations in the corpus

Optimal Decision Trees for the Algorithm Selection Problem: Integer Programming Based Approaches (2019, methodology): “Algorithm 1 shows the pseudo-code for our approach called VND-ASP.”
Metaheuristic research: a comprehensive survey (2018, methodology): “…Search (Glover 1989) VCS Virus Colony Search (Li et al. 2016) VDSA Variable Depth Search Algorithm (Bouhmala 2015) VNS Variable Neighborhood Search (Mladenovi and Hansen 1997) VSA Vortex Search Algorithm (Doan and lmez 2015) WCA Water Cycle Algorithm (Sadollah et al. 2015) WDO Wind Driven…”
Electric Vehicle Routing and Charging/Discharging under Time-Variant Electricity Prices (2020, methodology): “The variable neighborhood search heuristic was proposed by Mladenović and Hansen (1997).”
Data-Driven Surrogate-Assisted Multiobjective Evolutionary Optimization of a Trauma System (2016, background): “If among those solutions in NB3, solution [2001] dominates all other solutions, it will be the final output of the VNS.”
Solving Mixed Integer Programs Using Neural Networks (2020, methodology): “For this reason one could reasonably call our method a hybrid of diving and neighborhood search (Mladenović and Hansen 1997, Hansen et al. 2010), but for brevity we refer to it simply as Neural Diving.”
Variable neighborhood search: basics and variants (2017, methodology): “Variable neighborhood search (VNS) is a metaheuristic proposed by Mladenović and Hansen in 1997 [42].”
Total tardiness minimization in permutation flow shops: a simple approach based on a variable greedy algorithm (2008, methodology): “The present paper proposes a different procedure for overcoming local optima, which is based on the VNS concept (Mladenović and Hansen 1997).”
Metaheuristic design of feedforward neural networks: A review of two decades of research (2017, methodology): “For example, algorithms like simulated annealing (SA) [96], tabu search (TS) [97], variable neighborhood search (VNS) [98], greedy randomized adaptive search (GRAP) [99], etc., improves a single solution by searching around its neighborhood and continue to improve the solution until a satisfactory s”
Combinatorial optimization and reasoning with graph neural networks (2021, background): “, 2019), tabu search (Glover and Laguna, 1998; Laguna, 2018), genetic algorithms (Kramer, 2017), variable neighborhood search (Mladenović and Hansen, 1997; Hansen et al., 2019), all of which are designed to help escape local minima.”
A research survey: review of flexible job shop scheduling techniques (2016, unknown): “VNS was first proposed by Mladenović and Hansen (1997).”