Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
Mobile Radio Networks: Networking and Protocols
Mobile Radio Networks: Networking and Protocols
The GSM System for Mobile Communications
The GSM System for Mobile Communications
Population-Based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning
Removing the Genetics from the Standard Genetic Algorithm
Removing the Genetics from the Standard Genetic Algorithm
Metaheuristics in combinatorial optimization: Overview and conceptual comparison
ACM Computing Surveys (CSUR)
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
Parallelism and evolutionary algorithms
IEEE Transactions on Evolutionary Computation
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The frequency assignment problem (FAP) is a well-known problem in Operations Research which includes different mathematical models. This importance comes from the relevance of frequency planning in current GSM operators. The FAP problem basically tries to minimize the number of interferences caused when a limited number of frequencies has to be assigned to a quite high number of transceivers. In this work, we focus on solving this problem for a realistic-sized, real-world GSM network using a parallelized version of the PBIL algorithm. PBIL (Population-Based Incremental Learning) is based on genetic algorithms and competitive learning, being a population evolution model based on probabilistic models. Using cluster computing, we have parallelized the PBIL algorithm fixed to the FAP problem, and the analysis of the results proves that we have reached a double goal: on the one hand, with the parallelized version of the algorithm, its execution time is reduced down to the optimum values; and on the other hand, we prove that using a distributed island model applied to PBIL, the results for the network planning are better than the ones obtained with the sequential version.