Search Intensification in Metaheuristics for Solving the Automatic Frequency Problem in GSM
Frequency assignment is a well-known problem in Operations Research for which different mathematical models exist depending on the application specific conditions. However, most of these models are far from considering actual technologies currently deployed in GSM networks (e.g. frequency hopping). These technologies allow the network capacity to be actually increased to some extent by avoiding the interferences provoked by channel reuse due to the limited available radio spectrum, thus improving the Quality of Service (QoS) for subscribers and an income for the operators as well. Therefore, the automatic generation of frequency plans in real GSM networks is of great importance for present GSM operators. This is known as the Automatic Frequency Planning (AFP) problem. In this work, we focus on solving this problem for a realistic-sized, real-world GSM network with several metaheuristics featuring enhanced intensification strategies, namely (1,λ) Evolutionary Algorithms and Simulated Annealing. This research line has been investigated because these algorithms have proven to perform the best for this problem in the literature. By using the same basic specialized operators and the same computational effort, SA has shown to outperform EAs by computing frequency plans which provoke lower interferences.
KeywordsReal-World Frequency Planning Evolutionary Algorithms Simulated Annealing
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