Scatter tabu search for multiobjective clustering problems

R Caballero, M Laguna (), R Martí and J Molina
Additional contact information
R Caballero: University of Málaga
M Laguna: University of Colorado
R Martí: University of Valencia
J Molina: University of Málaga

Journal of the Operational Research Society, 2011, vol. 62, issue 11, 2034-2046

Abstract: Abstract We propose a hybrid heuristic procedure based on scatter search and tabu search for the problem of clustering objects to optimize multiple criteria. Our goal is to search for good approximations of the efficient frontier for this class of problems and provide a means for improving decision making in multiple application areas. Our procedure can be viewed as an extension of SSPMO (a scatter search application to nonlinear multiobjective optimization) to which we add new elements and strategies specially suited for combinatorial optimization problems. Clustering problems have been the subject of numerous studies; however, most of the work has focused on single-objective problems. Clustering using multiple criteria and/or multiple data sources has received limited attention in the operational research literature. Our scatter tabu search implementation is general and tackles several problems classes within this area of combinatorial data analysis. We conduct extensive experimentation to show that our method is capable of delivering good approximations of the efficient frontier for improved analysis and decision making.

Keywords: SSPMO; multiobjective optimization; scatter and tabu search; combinatorial data analysis; partitioning (search for similar items in EconPapers)
Date: 2011
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Citations: View citations in EconPapers (3)

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DOI: 10.1057/jors.2010.180

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