Bounded Variables nonlinear Multiple Criteria Optimization using Scatter search

This paper introduces an adaptation of multiple criteria scatter search to deal with nonlinear continuous vector optimization problems on bounded variables, applying Tabu Search approach as diversification generator method. Frequency memory and another escape mechanism are used to diversify the sear...

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Autor Principal: Beausoleil, Ricardo P.
Formato: Artículo
Idioma: Español
Publicado: 2015
Acceso en línea: http://revistas.ucr.ac.cr/index.php/matematica/article/view/235
http://hdl.handle.net/10669/12879
Sumario: This paper introduces an adaptation of multiple criteria scatter search to deal with nonlinear continuous vector optimization problems on bounded variables, applying Tabu Search approach as diversification generator method. Frequency memory and another escape mechanism are used to diversify the search. A relation Pareto is apply in order to designate a subset of the best generated solutions to be reference solutions. A choice function called Kramer Selection is used to divide the reference solution in two subsets. The Euclidean distance is used as a measure of dissimilarity in order to find diverse solutions to complement the subsets of high quality current Pareto solutions to be combined. Convex combination is used as a combined method. The performance of this approach is evaluated on several test problems taken from the literature.