Mobile Radio Networks: Networking and Protocols
Mobile Radio Networks: Networking and Protocols
The GSM System for Mobile Communications
The GSM System for Mobile Communications
Scatter Search: Methodology and Implementations in C
Scatter Search: Methodology and Implementations in C
Population-Based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning
ACO vs EAs for solving a real-world frequency assignment problem in GSM networks
Proceedings of the 9th 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
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In this paper we study two different meta-heuristics to solve a real-word frequency assignment problem (FAP) in GSM networks. We have used a precise mathematical formulation in which the frequency plans are evaluated using accurate interference information coming from a real GSM network. We have developed an improved version of the scatter search (SS) algorithm in order to solve this problem. After accurately tuning this algorithm, it has been compared with a version fixed for the FAP problem of the population-based incremental learning (PBIL) algorithm. The results show that SS obtains better frequency plannings than PBIL for all the experiments performed.