New heuristics for the maximum diversity problem

Authors:
Geiza C. Silva;Marcos R. Andrade;Luiz S. Ochi;Simone L. Martins;Alexandre Plastino
Affiliations:
Departamento de Ciência da Computação, Universidade Federal Fluminense, Niterói, Brazil 24210-240;Departamento de Ciência da Computação, Universidade Federal Fluminense, Niterói, Brazil 24210-240;Departamento de Ciência da Computação, Universidade Federal Fluminense, Niterói, Brazil 24210-240;Departamento de Ciência da Computação, Universidade Federal Fluminense, Niterói, Brazil 24210-240;Departamento de Ciência da Computação, Universidade Federal Fluminense, Niterói, Brazil 24210-240
Venue:
Journal of Heuristics
Year:
2007

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Abstract

The maximum diversity problem (MDP) consists of identifying, in a population, a subset of elements, characterized by a set of attributes, that present the most diverse characteristics among the elements of the subset. The identification of such solution is an NP-hard problem. Some heuristics are available to obtain approximate solutions for this problem. In this paper, we propose different GRASP heuristics for the MDP, using distinct construction procedures and including a path-relinking technique. Performance comparison among related work and the proposed heuristics is provided. Experimental results show that the new GRASP heuristics are quite robust and are able to find high-quality solutions in reasonable computational times.