Heuristics and metaheuristics for the maximum diversity problem

  • Authors:
  • Rafael Martí;Micael Gallego;Abraham Duarte;Eduardo G. Pardo

  • Affiliations:
  • Departamento de Estadística e Investigación Operativa, Universidad de Valencia, Valencia, Spain;Departamento de Ciencias de la Computación, Universidad Rey Juan Carlos, Madrid, Spain;Departamento de Ciencias de la Computación, Universidad Rey Juan Carlos, Madrid, Spain;Departamento de Ciencias de la Computación, Universidad Rey Juan Carlos, Madrid, Spain

  • Venue:
  • Journal of Heuristics
  • Year:
  • 2013

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Abstract

This paper presents extensive computational experiments to compare 10聽heuristics and 20聽metaheuristics for the maximum diversity problem (MDP). This problem consists of selecting a subset of maximum diversity from a given set of elements. It arises in a wide range of real-world settings and we can find a large number of studies, in which heuristic and metaheuristic methods are proposed. However, probably due to the fact that this problem has been referenced under different names, we have only found limited comparisons with a few methods on some sets of instances.This paper reviews all the heuristics and metaheuristics for finding near-optimal solutions for the MDP. We present the new benchmark library MDPLIB, which includes most instances previously used for this problem, as well as new ones, giving a total of聽315. We also present an exhaustive computational comparison of the 30聽methods on the MDPLIB. Non-parametric statistical tests are reported in our study to draw significant conclusions.