VNS variants for the Max-Mean Dispersion Problem

Abstract

In this paper we propose three different VNS variants to solve the Max-Mean Dispersion Problem. This problem consists of selecting a set of elements in such a way that the average distance between them is maximized. To tackle this problem, we propose a Basic Variable Neighborhood Search (BVNS), a Variable Neighborhood Descent (VND) and a Generalized Variable Neighborhood Search (GVNS) that hybridizes the previous two methods. Experimentation on previously reported instances shows that the Variable Neighborhood Search methodology is able to obtain solutions of high quality when compared with state of the art procedures.