Paper Info
Title
Using omnidirectional BTS and different evolutionary approaches to solve the RND Problem
Abstract
RND (Radio Network Design) is an important problem in mobile telecommunications (for example in mobile/cellular telephony), being also relevant in the rising area of sensor networks. This problem consists in covering a certain geographical area by using the smallest number of radio antennas achieving the biggest cover rate. To date, several radio antenna models have been used: square coverage antennas, omnidirectional antennas that cover a circular area, etc. In this work we use omnidirectional antennas. On the other hand, RND is an NP-hard problem; therefore its solution by means of evolutionary algorithms is appropriate. In this work we study different evolutionary approaches to tackle this problem. PBIL (Population-Based Incremental Learning) is based on genetic algorithms and competitive learning (typical in neural networks). DE (Differential Evolution) is a very simple population-based stochastic function minimizer used in a wide range of optimization problems, including multi-objective optimization. SA (Simulated Annealing) is a classic trajectory descent optimization technique. Finally, CHC is a particular class of evolutionary algorithm which does not use mutation and relies instead on incest prevention and disruptive crossover. Due to the complexity of such a large analysis including so many techniques, we have used not only sequential algorithms, but also grid computing with BOINC in order to execute thousands of experiments in only several days using around 100 computers.
Year
DOI
Venue
2007
10.1007/978-3-540-75867-9_107
EUROCAST
Keywords
Field
DocType
different evolutionary approach,multi-objective optimization,important problem,circular area,omnidirectional bts,classic trajectory descent optimization,certain geographical area,optimization problem,omnidirectional antenna,evolutionary algorithm,np-hard problem,rnd problem,np hard problem,multi objective optimization,differential evolution,sensor network,mobile telecommunication,neural network,grid computing,competitive learning,genetic algorithm,simulated annealing
Simulated annealing,Omnidirectional antenna,Population,Crossover,Evolutionary algorithm,Computer science,Differential evolution,Artificial intelligence,Optimization problem,Genetic algorithm,Machine learning
Conference
Volume
ISSN
ISBN
4739
0302-9743
3-540-75866-6
Citations 
PageRank 
References 
1
0.35
10
Authors
6
Name
Order
Citations
PageRank
Miguel A. Vega-Rodríguez1741113.05
Juan A. Gómez-Pulido220221.36
Enrique Alba33796242.34
David Vega-Pérez481.70
Silvio Priem-Mendes5203.04
Guillermo Molina6867.05