Nonlinear negotiation approaches for complex-network optimization: a study inspired by Wi-Fi channel assignment
Autores
Marsá Maestre, Iván; Hoz de la Hoz, Enrique de la; Giménez Guzmán, José Manuel; Orden Martín, David; Klein, MarkIdentificadores
Enlace permanente (URI): http://hdl.handle.net/10017/29937DOI: 10.1007/978-3-319-57285-7_4
ISBN: 978-3-319-57284-0
Editor
Springer
Fecha de publicación
2017-07-20Fecha fin de embargo
2017-07-20Filiación
Universidad de Alcalá. Departamento de Automática; Universidad de Alcalá. Departamento de Física y Matemáticas. Unidad docente MatemáticasPatrocinadores
Ministerio de Economía y Competitividad
Universidad de Alcalá
Cita bibliográfica
Marsá Maestre, I., Hoz de la Hoz, E., Giménez Guzmán, J.M., Orden Martín, D. & Klein, M. 2017, "Nonlinear negotiation approaches for complex-network optimization: a study inspired by Wi-Fi channel assignment ", Conflict Resolution in Decision Making: Second International Workshop, COREDEMA 2016, vol. 10238, pp. 51-65.
Proyectos
info:eu-repo/grantAgreement/MINECO//TIN 2014-61627-EXP/ES/DIVIDE AND NOT CONQUER-COMPORTAMIENTOS EMERGENTES EN REDES COMPLEJAS EGOISTAS/
info:eu-repo/grantAgreement/MINECO//MTM2014-54207-P/ES/COMBINATORIA Y COMPLEJIDAD DE ESTRUCTURAS GEOMETRICAS DISCRETAS/
info:eu-repo/grantAgreement/MINECO//TEC2013-45183-R/ES/INTELIGENCIA COLECTIVA PARA LA NAVEGACION INTELIGENTE DE TRAFICO VEHICULAR/
info:eu-repo/grantAgreement/UAH//CCG2015%2FEXP-053
Tipo de documento
info:eu-repo/semantics/bookPart
Versión
info:eu-repo/semantics/acceptedVersion
Derechos
Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
(c) Springer International Publishing AG, 2017
Derechos de acceso
info:eu-repo/semantics/openAccess
Resumen
In this paper, we study a problem family inspired by a prominent network optimization problem (graph coloring), enriched and extended towards a real-world application (Wi-Fi channel assignment). We propose a utility model based on this scenario, and we generate an extensive set of test cases, against which we run both a complete information optimizer and two nonlinear negotiation approaches {a hill-climber and an approach based on simulated annealing (SA). We show that, for the larger-scale scenarios, the SA negotiation approach significantly outperforms the optimizer while running in roughly one tenth of the computation time. Also, we point out interesting patterns regarding the relative performance of the two approaches depending on the properties of the underlying graphs.
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