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This paper presents a multiobjective approach for the Frequency Assignment Problem (FAP) in a real-world GSM network. Indeed, nowadays in GSM systems, the FAP stills continues to be a critical task for the mobile communication operators. In this work we propose a new method to address the FAP by applying the Differential Evolution (DE) algorithm in its multiobjective optimization, using the concept of Pareto Tournaments (DEPT). We present the results obtained in the tuning process of the DEPT parameters. Two distinct real-world instances of the problem - being currently operating - were tested with DEPT algorithm. Therefore, with this multiobjective approach for the FAP we are contributing to a really important applicability. | 522.57 KB | Adobe PDF |
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Abstract(s)
This paper presents a multiobjective approach for the Frequency Assignment Problem (FAP) in a real-world GSM network. Indeed, nowadays in GSM systems, the FAP stills continues to be a critical task for the mobile communication operators. In this work we propose a new method to address the FAP by applying the Differential Evolution (DE) algorithm in its multiobjective optimization, using the concept of Pareto Tournaments (DEPT). We present the results obtained in the tuning process of the DEPT parameters. Two distinct real-world instances of the problem - being currently operating - were tested with DEPT algorithm. Therefore, with this multiobjective approach for the FAP we are contributing to a really important applicability.
Description
10th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2009, 23 September 2009 through 26 September 2009 - Code 79260
Keywords
Pareto Front Multiobjective Optimization Variable Neighborhood Search Channel Separation Frequency Assignment Problem
Citation
da Silva Maximiano, M., Vega-Rodríguez, M.A., Gómez-Pulido, J.A., Sánchez-Pérez, J.M. (2009). Parameter Analysis for Differential Evolution with Pareto Tournaments in a Multiobjective Frequency Assignment Problem. In: Corchado, E., Yin, H. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2009. IDEAL 2009. Lecture Notes in Computer Science, vol 5788. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04394-9_98.
Publisher
Springer Nature
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CC License
Without CC licence