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Nonlinear Negotiation Approaches for Complex-Network Optimization: A Study Inspired by Wi-Fi Channel Assignment

  • Ivan Marsa-MaestreEmail author
  • Enrique de la Hoz
  • Jose Manuel Gimenez-Guzman
  • David Orden
  • Mark Klein
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10238)

Abstract

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.

Keywords

Access Point Cluster Coefficient Channel Assignment Graph Coloring Hill Climber 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

This work has been supported by the Spanish Ministry of Economy and Competitiveness grants TIN2016-80622-P, TIN2014-61627-EXP, MTM2014-54207, and TEC2013-45183-R and by the University of Alcalá through CCG2015/EXP-053.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Ivan Marsa-Maestre
    • 1
    Email author
  • Enrique de la Hoz
    • 1
  • Jose Manuel Gimenez-Guzman
    • 1
  • David Orden
    • 2
  • Mark Klein
    • 3
  1. 1.Computer Engineering DepartmentUniversity of AlcalaAlcalá de HenaresSpain
  2. 2.Department of Physics and MathematicsUniversity of AlcalaAlcalá de HenaresSpain
  3. 3.Center for Collective IntelligenceMITCambridgeUSA

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