Bounded Variables nonlinear Multiple Criteria Optimization using Scatter search
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| Autor: | |
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| Formato: | artículo original |
| Estado: | Versión publicada |
| Fecha de Publicación: | 2004 |
| Descripción: | 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. |
| País: | Portal de Revistas UCR |
| Institución: | Universidad de Costa Rica |
| Repositorio: | Portal de Revistas UCR |
| Lenguaje: | Español |
| OAI Identifier: | oai:portal.ucr.ac.cr:article/235 |
| Acceso en línea: | https://revistas.ucr.ac.cr/index.php/matematica/article/view/235 |
| Access Level: | acceso abierto |
| Palabra clave: | Tabu Search Scatter Search Nonlinear Optimization Búsqueda Tabú Búsqueda Dispersa Optimización No Lineal |