Optimal switch location in mobile communication networks using hybrid genetic algorithms

  • Authors:
  • Sancho Salcedo-Sanz;Jose A. Portilla-Figueras;Emilio G. Ortiz-García;Angel M. Pérez-Bellido;Christopher Thraves;Antonio Fernández-Anta;Xin Yao

  • Affiliations:
  • Department of Signal Theory and Communications, Universidad de Alcalá, 28871 Alcalá de Henares, Madrid, Spain;Department of Signal Theory and Communications, Universidad de Alcalá, 28871 Alcalá de Henares, Madrid, Spain;Department of Signal Theory and Communications, Universidad de Alcalá, 28871 Alcalá de Henares, Madrid, Spain;Department of Signal Theory and Communications, Universidad de Alcalá, 28871 Alcalá de Henares, Madrid, Spain;Department of Mathematical Engineering, Universidad de Chile, Chile;Grupo de Sistemas y Comunicaciones (GSyC), Laboratorio de Algoritmia Distribuida y Redes (LADyR), Universidad Rey Juan Carlos, Madrid, Spain;Centre of Excellence for Research in Computational Intelligence and Applications (CERCIA), School of Computer Science, the University of Birmingham and Nature Inspired Computation and Applications ...

  • Venue:
  • Applied Soft Computing
  • Year:
  • 2008

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Abstract

The optimal positioning of switches in a mobile communication network is an important task, which can save costs and improve the performance of the network. In this paper we propose a model for establishing which are the best nodes of the network for allocating the available switches, and several hybrid genetic algorithms to solve the problem. The proposed model is based on the so-called capacitated p-median problem, which have been previously tackled in the literature. This problem can be split in two subproblems: the selection of the best set of switches, and a terminal assignment problem to evaluate each selection of switches. The hybrid genetic algorithms for solving the problem are formed by a conventional genetic algorithm, with a restricted search, and several local search heuristics. In this work we also develop novel heuristics for solving the terminal assignment problem in a fast and accurate way. Finally, we show that our novel approaches, hybridized with the genetic algorithm, outperform existing algorithms in the literature for the p-median problem.