On integrated ant colony optimization strategies for improved channel allocation in large scale wireless communications

Authors:
P. M. Papazoglou;D. A. Karras;R. C. Papademetriou
Affiliations:
Department of Informatics & Computer Technology, Lamia Institute of Technology, Greece and Department of Electronic & Computer Engineering, University of Portsmouth, United Kingdom;Department of Automation Engineering, Chalkis Institute of Technology, Greece;Department of Electronic & Computer Engineering, University of Portsmouth, United Kingdom
Venue:
MAMECTIS'08 Proceedings of the 10th WSEAS international conference on Mathematical methods, computational techniques and intelligent systems
Year:
2008

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Abstract

Recently, various sophisticated strategies adapted to current network conditions have been proposed for channel allocation based on intelligent techniques such as evolutionary and genetic algorithms. These approaches constitute heuristic solutions to resource management problems in modern cellular systems. On the other hand, the ant colony optimization approach has been proposed for solving several optimization problems with promising results. It would be interesting, therefore, to investigate its application in resource management problems of wireless systems. A comprehensive and efficient heuristic approach for solving the channel allocation problem in large scale wireless communication systems, based on intelligent techniques and especially on integrating multi-agent methodology and ant colony optimization strategies, is herein proposed. Moreover, important implementation issues of ant colony optimization within a multi-agent simulation system are investigated. In addition, multi-agent realization issues such as thread execution sequence definition are also presented. An initial but comprehensive simulation study has been conducted and the simulation results show the performance improvement of the proposed ant colony optimization algorithm as well as the multi-agent modelling approach with respect to traditional network performance statistics.