Variable Neighbourhood Search

Abstract

The basic idea of VNS is the change of neighbourhoods in the search for a better solution. VNS proceeds by a descent method to a local minimum exploring then, systematically or at random, increasingly distant neighbourhoods of this solution. Each time, one or several points within the current neighbourhood are used as initial solutions for a local descent. The method jumps from the current solution to a new one if and only if a better solution has been found. Therefore, VNS is not a trajectory following method (as Simulated Annealing or Tabu Search) and does not specify forbidden moves. In this work, we show how the variable neighbourhood search metaheuristic can be applied to train an artificial neural network. We define a set of nested neighbourhoods and follow the basic VNS scheme to carry out our experiments