@Article{OliveiraChavLore:2017:CoTwHy,
author = "Oliveira, Rudinei Martins and Chaves, Antonio Augusto and Lorena,
Luiz Antonio Nogueira",
affiliation = "{Universidade Federal de S{\~a}o Paulo (UNIFESP)} and
{Universidade Federal de S{\~a}o Paulo (UNIFESP)} and {Instituto
Nacional de Pesquisas Espaciais (INPE)}",
title = "A comparison of two hybrid methods for constrained clustering
problems",
journal = "Applied Soft Computing Journal",
year = "2017",
volume = "54",
pages = "256--266",
month = "May",
keywords = "BRKGA, Clustering, Column generation.",
abstract = "This paper proposes two hybrid heuristics to solve the constrained
clustering problem. This problem consists of partitioning a set of
objects into clusters with similar members that satisfy must-link
and cannot-link constraints. A must-link constraint indicates that
two selected objects must be in the same cluster, and cannot-link
constraint means that two selected objects must be in distinct
clusters. The two proposed hybrid methods are biased random key
genetic algorithm (BRKGA) with local search (LS) heuristic and
column generation (CG) with path-relinking (PR) and local search
(LS) heuristic. Computational experiments considering instances
available in the literature are presented to demonstrate the
efficacy of the proposed methods to solve the constrained
clustering problem. Moreover, the results of the CG and BRKGA are
compared with the CCCG, CP and CPRBBA method.",
doi = "10.1016/j.asoc.2017.01.023",
url = "http://dx.doi.org/10.1016/j.asoc.2017.01.023",
issn = "1568-4946 and 1872-9681",
language = "en",
targetfile = "oliveira_comparison.pdf",
urlaccessdate = "02 maio 2024"
}