Genetic Tabu search for robust fixed channel assignment under dynamic traffic data

The contribution of this work is twofold. Firstly, we introduce a new channel assignment model for GSM radio networks. In this model both spatial and temporal variations of traffic are taken into account in order to improve network capacity and robustness. Secondly, using this model, we develop an original and effective hybrid algorithm to get high quality frequency plans. This algorithm combines a problem specific crossover and a Tabu search procedure. The proposed model and hybrid algorithm are evaluated using both artificial and real data. Computational results allow us to confirm the effectiveness of the proposed approach.

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