Neigborhood Selection in Variable Neighborhood Search

TL;DR

This paper investigates how to effectively select and order neighborhoods in Variable Neighborhood Search to improve optimization performance, focusing on the benefits of lookahead strategies for neighborhood switching.

Contribution

It analyzes criteria for choosing and sequencing neighborhoods in VNS and explores the advantages of lookahead methods in neighborhood switching.

Findings

Lookahead strategies can enhance neighborhood switching effectiveness.

Optimal neighborhood selection depends on problem characteristics.

Systematic neighborhood design improves VNS performance.

Abstract

Variable neighborhood search (VNS) is a metaheuristic for solving optimization problems based on a simple principle: systematic changes of neighborhoods within the search, both in the descent to local minima and in the escape from the valleys which contain them. Designing these neighborhoods and applying them in a meaningful fashion is not an easy task. Moreover, an appropriate order in which they are applied must be determined. In this paper we attempt to investigate this issue. Assume that we are given an optimization problem that is intended to be solved by applying the VNS scheme, how many and which types of neighborhoods should be investigated and what could be appropriate selection criteria to apply these neighborhoods. More specifically, does it pay to "look ahead" (see, e.g., in the context of VNS and GRASP) when attempting to switch from one neighborhood to another?