Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
An Ants heuristic for the frequency assignment problem
Future Generation Computer Systems
Mobile Radio Networks: Networking and Protocols
Mobile Radio Networks: Networking and Protocols
The GSM System for Mobile Communications
The GSM System for Mobile Communications
Metaheuristics in combinatorial optimization: Overview and conceptual comparison
ACM Computing Surveys (CSUR)
Ant Colony Optimization
Optimized planning of frequency hopping in cellular networks
Computers and Operations Research
EvoCOP'07 Proceedings of the 7th European conference on Evolutionary computation in combinatorial optimization
The hyper-cube framework for ant colony optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Metaheuristics for solving a real-world frequency assignment problem in GSM networks
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Comparing Hybrid Versions of SS and DE to Solve a Realistic FAP Problem
HAIS '08 Proceedings of the 3rd international workshop on Hybrid Artificial Intelligence Systems
Solving a Realistic FAP Using GRASP and Grid Computing
GPC '09 Proceedings of the 4th International Conference on Advances in Grid and Pervasive Computing
Using cluster computing to solve a real-world FAP problem
PDCN '08 Proceedings of the IASTED International Conference on Parallel and Distributed Computing and Networks
SS vs PBIL to solve a real-world frequency assignment problem in GSM networks
Evo'08 Proceedings of the 2008 conference on Applications of evolutionary computing
IDEAL'09 Proceedings of the 10th international conference on Intelligent data engineering and automated learning
Elementary landscapes of frequency assignment problems
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Population-ACO for the automotive deployment problem
Proceedings of the 13th annual conference on Genetic and evolutionary computation
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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.