Variantes del algoritmo CHC para proyectar redes de radio frecuencia en comunicaciones inalámbricas

Using variants of CHC Algorithms in the Design of Radio Frequency Networks in Wireless Communications.

Daniel Antonio Molina, Daniel Raul Pandolfi, Norma Andrea Villagra, Guillermo Leguizamón

Código

ICT-UNPA-119-2015

Resumen


En el presente trabajo se aplica una serie de versiones del algoritmo genético no convencional denominado Cross generational elitist selection Heterogeneous recombination Cataclysmic mutation algorithm (CHC ) para resolver el problema de diseño de red de radio (RND). Se utiliza un conjunto de algoritmos genéticos para realizar una comparativa de rendimiento de los algoritmos propuestos. Se emplea una función objetivo basada en la eficiencia de iluminación de la señal. Se utiliza la variabilidad genética de la población como parámetro de convergencia y detección de incesto y se propone el uso de la variabilidad del mejor individuo como mecanismo de sacudida. Esto permite generar poblaciones dinámicas conforme a las soluciones más promisorias generando diferentes espacios de búsqueda. Los resultados obtenidos por los algoritmos propuestos son satisfactorios.


Palabras clave


CHC; RND; variabilidad genética


Abstract


In this paper we apply to solve the Radio Network Design problem (RND) a series  of the non-conventional genetic algorithms called Cross generational elitist selection Heterogeneous recombination Cataclysmic mutation (CHC). A set of genetic algorithms is used to perform a comparative performance of the proposed algorithms. An objective function based on signal coverage efficiency is used. Genetic variability of the population is used for both, as a parameter of convergence and detection of incest. Furthermore the variability of the best individual is proposed as a shaking mechanism. This allows generating dynamic populations according to the most promising solutions generating different search spaces. The results obtained by the proposed algorithms are satisfactory.


Keywords


CHC; RND; Genetic Variability


Área


Ingeniería y Tecnología

Resolución


0746/15-R-UNPA

Fecha de Aprobación


2015-08-25

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Referencias


ALBA E. and Chicano F. 2005, On the behavior of parallel genetic algorithms for optimal placement of antennae in telecommunications, Int. J. Found. Comput. Sci., vol. 16, pp. 86–90. https://doi.org/10.1142/S0129054105003029

ANDERSON H.R. and McGeehan J.P. 1994, Optimizing Microcell Base Station Locations Using Simulated Annealing Techniques. In Proceedings 44th IEEE Conference on Vehicular Technology, pages 858-862. https://doi.org/10.1109/VETEC.1994.345212

CALEGARI (a) P., Guidec F., Kuonen P. 1997, and Kobler D., Parallel Island Based Genetic Algorithm for Radio Network Design. In Journal of Parallel and Distributed Computing (47), pages 86-89.

CALEGARI (b) P., Guidec F., Kuonen P. 1997, and Wagner D. Genetic Approach to Radio Network 22 Optimizations for Mobile Systems. In Proceedings 47th IEEE Conference on Vehicular Technology, volume 2, pages 755- 759.

CELLI G., Costamagna E., and Fanni A. 1995, Genetic Algorithms for Telecommunication Network Optimization, presented at IEEE Int. Conf. Syst., Man and Cybernetics. https://doi.org/10.1109/ICSMC.1995.537939

CORNE W., Oates M., Smith G. 2000, Telecommunications Optimization: Heuristic and Adaptive Techniques. John Wiley & Sons Ltd, ISBNs: 0-471-98855-3 (Hardback); 0-470-84163X.

ESHELMAN L. 1991, The CHC Adaptive Search Algorithm: How to Have Safe Search When Engaging in Nontraditional Genetic Recombination. In Foundations of Genetic Algorithms, pages 265–283. Morgan Kaufmann.

FRITSCH Th., Tutschku K., Leibnitz K. 1995, Field Strength Prediction by Ray Tracing for Adaptive Base Station Positioning in Mobile Comunication Networks.

GAMST A., E.G. Zinn, Beck R., Simon and R. 1986, Cellular Radio Network Planning. En revista IEEE Aerospace and Electronic Systems Magazine, (1):8, 11.

GOLDBERG (a) D. 1989, Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley.

GOLDBERG (b) D. 2002, Design of Innovation: Lessons From and For Competent Genetic Algorithms, Kluwer, Boston, MA.

KIRKPATRICK S, Gelatt C. D., and Vecchi M. P. 1983, Optimization by simulated annealing. Science , New Series, Vol. 220, No. 4598. pp. 671-680.

LUNA F., Durillo J. J., Nebro A. J., and Alba E. 2010, Evolutionary algorithms for solving the automatic cell planning problem: a survey. Engineering Optimization, 42(7):671–690, Dec. 2010. https://doi.org/10.1080/03052150903426850

MENDES S., Molina G., Vega-Rodríguez M., Gómez-Pulido J., Sáez Y., Miranda G., Segura C., Alba E., P. Isasi, León C., and Sánchez-Pérez J. 2009, Benchmarking a Wide Spectrum of Metaheuristic Techniques for the Radio Network Design Problem. IEEE Transactions on Evolutionary Computation, vol. 13, no. 5. https://doi.org/10.1109/TEVC.2009.2023448

MEUNIER H., Talbi E. G., and Reininger P. 2000, A Multiobjective Genetic Algorithm for Radio Network Optimization, presented at Congr. Evol. Comput, 2000. https://doi.org/10.1109/CEC.2000.870312

NEBRO A. J., Alba E., Molina G., Chicano F., and J. J. Luna, Francisco y Durillo J. 2007. Optimal antenna placement using a new multi-objective chc algorithm. In Proceedings of the 9th annual conference on Genetic and evolutionary computation, pages 876–883. ACM. https://doi.org/10.1145/1276958.1277128

PAL S.K. and Wang P.P.1996, Genetic Algorithms for Pattern Recognition. Computer science mathematics. Taylor & Francis.

PARMEE I.C. 2001, Evolutionary and Adaptive Computing in Engineering Design: With 98 Figures. Springer London. https://doi.org/10.1007/978-1-4471-0273-1

RAPPAPORT T. 1996, Wireless communications principles and practice. 1ra ed. New Jersey: Prentice Hall, ISBN: 0-13-375536-3.

SALLING W. 2008, Comunicaciones y Redes de computadores, 7ma edición, Ed Pearson Prentince Hall.

SASTRY K., Goldberg D., Kendall G. 2005, Search Methodologies: Introductory Tutorials in Optimization and Decision Support Techniques.

TALBI (a) E. 2009, Metaheuristics From Design To Implementation, Copyright ©2009 by John Wiley & Sons, Inc. ISBN: 978-0-470-27858-1.

TALBI (b) E. G., Cahon S., and Melab N. 2007, “Designing cellular networks using a parallel hybrid metaheuristic on the computational grid,” Comput. Commun., vol. 30, no. 4, pp. 698–713. https://doi.org/10.1016/j.comcom.2006.08.017

TREVIÑO CORTÉS J. 2003, Propagación de RF en las bandas: LF, MF, HF, VHF, UHF y VHF, Cap5. Modelos de Propagación.

TUTSCHKU K., Gerlich N., and Tran-Gia P. 1995, An integrated Approach to Cellular Network Planning, Institute of Computer Science, University of Wurzburg.

VEGA-RODRÍGUEZ M., Gómez-Pulido J., Alba E., Vega-Pérez D., Priem-Mendes S., Molina G. 2007, Evaluation of Different Metaheuristics Solving the RND Problem ,EvoWorkshops 2007, LNCS 4448, pp. 101–110.

VÉLEZ LANGS O. 2012, Enfoques no estándar de algoritmos evolutivos en un dilema de optimiza- ción. En Revista Mutis, Volumen 2, Número 2, pp. 126-138.

WATANABE S., Hiroyasu T., and Mikiand M. 2001, Parallel Evolutionary Multicriterion Optimization for Mobile Telecommunication Networks Optimization, presented at Eurogen 2001—Evol. Methods Design, Optimisation Control with Applicat. Ind. Problems Conf., Athens, Greece.




DOI: http://dx.doi.org/10.22305/ict-unpa.v7i2.134

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