Variable Neighborhood Search for the Vertex Separation Problem
The vertex separation problem belongs to a family of optimization problems in which the objective is to nd the best separator of vertices or edges in a generic graph. This optimization problem is strongly related to other well-known graph problems; such as the Path-Width, the Node Search Number or t...
| Autores: | , , , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2012 |
| País: | España |
| Institución: | Universidad Rey Juan Carlos |
| Repositorio: | BURJC-Digital. Repositorio Institucional de la Universidad Rey Juan Carlos |
| OAI Identifier: | oai:dnet:burjcdigital::234c1af01dacaa5e1f46e0f5bdb8cf1f |
| Acceso en línea: | http://hdl.handle.net/10115/12378 |
| Access Level: | acceso abierto |
| Palabra clave: | Informática Estadística y Demografía Combinatorial Optimization Metaheuristics Variable Neigborhood Search Layout Problems 1203.17 Informática 52 Demografía 5207.10 Estadísticas de Poblaciones |
| Sumario: | The vertex separation problem belongs to a family of optimization problems in which the objective is to nd the best separator of vertices or edges in a generic graph. This optimization problem is strongly related to other well-known graph problems; such as the Path-Width, the Node Search Number or the Interval Thickness, among others. All of these optimization problems are NP-hard and have practical applications in VLSI, computer language compiler design or graph drawing. Up to know, they have been generally tackled with exact approaches, presenting polynomial-time algorithms to obtain the optimal solution for speci c types of graphs. However, in spite of their practical applications, these problems have been ignored from a heuristic perspective, as far as we know. In this paper we propose a pure 0-1 optimization model and a metaheuristic algorithm based on the variable neighborhood search methodology for the vertex separation problem on general graphs. Computational results show that small instances can be optimally solved with this optimization model and the proposed metaheuristic is able to nd high-quality solutions with a moderate computing time for large-scale instances. |
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