Variable neighbourhood search: methods and applications

  title={Variable neighbourhood search: methods and applications},
  author={Pierre Hansen and Nenad Mladenovi{\'c} and Jos{\'e} A. Moreno-P{\'e}rez},
Variable neighbourhood search (VNS) is a metaheuristic, or a framework for building heuristics, based upon systematic changes of neighbourhoods both in descent phase, to find a local minimum, and in perturbation phase to emerge from the corresponding valley. It was first proposed in 1997 and has since then rapidly developed both in its methods and its applications. In the present paper, these two aspects are thoroughly reviewed and an extensive bibliography is provided. Moreover, one section is… 
Adaptive multi-operator metaheuristics for quadratic assignment problems
A metaheuristic framework is proposed, called multi-operator metaheuristics, which allows the alternative or simultaneously usage of the two restarting methods, Iterated local search restarts the local search using perturbator operators, and the variable neighbourhood search alternates local search with various neighbourhoods.
Variable neighbourhood structures for cycle location problems
A New VNS Metaheuristic Using MADS as a Local Optimizer
We propose a new hybrid metaheuristic VNSMADS that is an implementation of the Variable Neighbourhood Search (VNS) algorithm with the Mesh Adaptive Direct Search (MADS) as the local search. Because
Recursive Variable Neighborhood Search
The experiments show that the proposed R-VNS outperforms the basic VNS by offering better solutions as well as higher convergence rate.
Local search metaheuristics for the critical node problem
Two metaheuristics are presented for the Critical Node Problem, that is, the maximal fragmentation of a graph through the deletion of k nodes, based on the Iterated Local Search and Variable Neighborhood Search frameworks, which are shown to outperform those currently available in literature.
Some applications of continuous variable neighbourhood search metaheuristic (mathematical modelling)
The results of this approach for censored quantile regression outperforms other methods described in the literature, and the near-optimal solutions are obtained in short running computational time.
Two Neighbourhood-based Approaches for the Set Covering Problem
This work addresses the Set Covering Problem with heuristic methods based on the well-known algorithms GRASP, Simulated Annealing and Variable Neighbourhood Descend along with a constructive heuristic based on a dynamic dispatching rule to generate initial feasible solutions.
An Adaptive VNS and Skewed GVNS Approaches for School Timetabling Problems
Two algorithms based on the Variable Neighborhood Search (VNS) metaheuristic are developed and probabilistically chooses the neighborhoods to do local searches, with the probability being higher for the more successful neighborhoods.
Variable Neighborhood Search for beginners
The first steps toward the use of the Variable Neighborhood Search metaheuristic are explained using an example taken from the search for extremal graphs that was used at in the early stage of the AutoGraphiX software.
A multi-arm bandit neighbourhood search for routing and scheduling problems
This work presents a D-MAB neighbourhood search which can be embedded within any meta- heuristic or hyperheuristic framework, and demonstrates the eectiveness of D- MABNS on two vehicle routing and scheduling problems, the real-world geographically distributed mainte- nance problem (GDMP) and the periodic vehicle routing problem (PVRP).


Parallel Variable Neighborhood Search
Several parallelization strategies for VNS have been proposed and compared on the large instances of the p-median problem.
Variable Neighborhood Decomposition Search
The resulting two level VNS, called Variable Neighborhood Decomposition Search (VNDS), is presented and illustrated on the p-median problem and shows VNDS improves notably upon VNS in less computing time, and gives much better results than Fast Interchange (FI), in the same time that FI takes for a single descent.
The Parallel Variable Neighborhood Search for the p-Median Problem
The use of interchange moves provides a simple implementation of the VNS algorithm for the p-Median Problem and several strategies for the parallelization of theVNS are considered and coded in C using OpenMP.
Variable Neighbourhood Search for Job Shop Scheduling Problems
An investigation on implementing VNS for job shop scheduling problems is carried out tackling benchmark suites collected from OR library to build the best local search and shake operations based on neighbourhood structure available.
A Variable Neighbourhood Search Algorithm for Job Shop Scheduling Problems
A variable neighbourhood search algorithm is proposed for Job Shop Scheduling problem with makespan criterion and it is concluded that the VNS implementation is better than many recently published works with respect to the quality of the solution.
Parallel variable neighbourhood search algorithms for job shop scheduling problems
The performance of various VNS algorithms and the efficiency of policies to follow in parallelization are revealed and the unilateral-ring topology, a noncentral parallelization method, is found as the most efficient policy.
Introduction to the special issue on variable neighborhood search
The papers that make up this special issue on Variable Neighborhood Search illustrate breadth and depth of research in variable neighborhood search since they show some advanced features of the metaheuristic.
Developments of Variable Neighborhood Search
After reviewing the basic scheme of VNS, several extensions aimed at solving large problem instances are surveyed, and issues in devising a VNS heuristic are discussed.
Variable neighborhood search: Principles and applications