ACO vs EAs for solving a real-world frequency assignment problem in GSM networks

Frequency planning is a very important task for current GSM operators. In this work we present a new mathematical formulation of the problem in which the frequency plans are evaluated by using accurate interference information coming from a real GSM network. We have developed an ant colony optimization (ACO) algorithm to tackle this problem. After accurately tuning this algorithm, it has been compared against a (1,10) Evolutionary Algorithm (EA). The results show that the ACO clearly outperforms the EA when using different time limits as stopping condition for a rather extensive comparison. Categories and Subject Descriptors I.2.8 [Artificial Intelligence]: Problem Solving, Control

• Type: Conference
• Year: 2007
• Where: GECCO
• Authors: Francisco Luna, Christian Blum, Enrique Alba, Antonio J. Nebro
• Keywords: Accurate Interference Information, Current Gsm Operators, GECCO 2007, Real Gsm Network