Grid Computing: Making the Global Infrastructure a Reality
Grid Computing: Making the Global Infrastructure a Reality
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
Metaheuristics for solving a real-world frequency assignment problem in GSM networks
Proceedings of the 10th annual conference on Genetic and evolutionary computation
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
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In this work we describe the methodology and results obtained when grid computing is applied to resolve a real-world frequency assignment problem (FAP) in GSM networks. We havJose used a precise mathematical formulation for this problem, which was developed in previous work, where the frequency plans are evaluated using accurate interference information taken from a real GSM network. We propose here a newly approach which lies in the usage of several versions of the GRASP (Greedy Randomized Adaptive Search Procedure) metaheuristic working together over a grid environment. Our study was divided into two stages: In the first one, we fixed the parameters of different GRASP variants using the grid so that each version obtained the best results possible when solving the FAP; then, in the second step, we developed a master-slave model using the grid where the GRASP tuned versions worked together as a team of evolutionary algorithms. Results show us that this approach obtains very good frequency plans when solving a real-world FAP.