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On the Design of Large-scale Cellular Mobile Networks Using Multi-population Memetic Algorithms

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Engineering Evolutionary Intelligent Systems

Part of the book series: Studies in Computational Intelligence ((SCI,volume 82))

Summary

This chapter proposes a proposes a multi-population memetic algorithm (MA) with migration and elitism to solve the problem of assigning cells to switches as a design step of large-scale mobile networks. Well-known in the literature as an NP-hard combinatorial optimization problem, this problem requires the recourse to heuristic methods which can practically lead to good feasible solutions, not necessarily optimal, the objective being rather to reduce the convergence time toward these solutions. Computational results obtained from extensive tests confirm the efficiency and the effectiveness of MA to provide good solutions in comparison with other heuristic methods well-known in the literature, specially for large-scale cellular mobile networks with a number of cells varying between 100 and 1000, and a number of switches varying between 5 and 10, that means the search space size is between 5100 and 101000.

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References

  1. Kang G, Sugeno M (1987) Fuzzy modeling. Trans Soc Instrum Control Eng 23(6cr):106–108

    Google Scholar 

  2. Oh SK, Pedrycz W (2000) Fuzzy identification by means of auto-tuning algorithm and its application to nonlinear systems. Fuzzy Sets Syst 115(2):205–230

    Article  MATH  MathSciNet  Google Scholar 

  3. Park BJ, Pedrycz W, Oh SK (2001) Identification of fuzzy models with the aid of evolutionary data granulation. IEE Proc-Control Theory Appl 148(5):406–418

    Article  Google Scholar 

  4. Oh SK, Pedrycz W, Park BJ (2002) Hybrid identification of fuzzy rule-based models. Int J Intell Syst 17(1):77–103

    Article  MATH  Google Scholar 

  5. Park BJ, Oh SK, Ahn TC, Kim HK (1999) Optimization of fuzzy systems by means of GA and weighting factor. Trans Korean Inst Electr Eng 48A(6):789–799 (In Korean)

    Google Scholar 

  6. Oh SK, Park CS, Park BJ (1999) On-line modeling of nonlinear process systems using the adaptive fuzzy-neural networks. Trans Korean Inst Electr Eng 48A(10):1293–1302 (In Korean)

    Google Scholar 

  7. Narendra KS, Parthasarathy K (1991) Gradient methods for the optimization of dynamical systems containing neural networks. IEEE Trans Neural Netw 2:252–262

    Article  Google Scholar 

  8. Goldberg DE (1989) Genetic algorithms in search, optimization & machine learning. Addison-wesley, Reading

    MATH  Google Scholar 

  9. Michalewicz Z (1996) Genetic Algorithms + Data Structures = Evolution Programs. Springer, Berlin Heidelberg Newyork

    MATH  Google Scholar 

  10. Holland JH (1975) Adaptation in natural and artificial systems. The University of Michigan Press, Ann Arbour

    Google Scholar 

  11. Pedrycz W, Peters JF (1998) Computational intelligence and software engineering. World Scientific, Singapore

    Google Scholar 

  12. Computational intelligence by programming focused on fuzzy neural networks and genetic algorithms. Naeha, Seoul (In Korean)

    Google Scholar 

  13. Horikawa S, Furuhashi T, Uchigawa Y (1992) On fuzzy modeling using fuzzy neural networks with the back propagation algorithm. IEEE Trans Neural Netw 3(5):801–806

    Article  Google Scholar 

  14. Oh SK, Pedrycz W (2002) The design of self-organizing polynomial neural networks. Inf Sci 141(3–4):237–258

    Article  MATH  Google Scholar 

  15. Oh SK, Pedrycz W, Park BJ (2003) Polynomial neural networks architecture: Analysis and Design. Comput Electr Eng 29(6):653–725

    Article  Google Scholar 

  16. Ohtani T, Ichihashi H, Miyoshi T, Nagasaka K (1998) Orthogonal and successive projection methods for the learning of neurofuzzy GMDH. Inf Sci 110:5–24

    Article  MathSciNet  Google Scholar 

  17. Ohtani T, Ichihashi H, Miyoshi T, Nagasaka K (1998) Structural learning with M-Apoptosis in neurofuzzy GMHD. In: Proceedings of the 7th IEEE International Conference on Fuzzy Systems:1265–1270

    Google Scholar 

  18. Ichihashi H, Nagasaka K (1994) Differential minimum bias criterion for neuro-fuzzy GMDH. In: Proceedings of 3rd International Conference on Fuzzy Logic Neural Nets and Soft Computing IIZUKA’94:171–172

    Google Scholar 

  19. Park BJ, Pedrycz W, Oh SK (2002) Fuzzy polynomial neural networks: hybrid architectures of fuzzy modeling. IEEE Trans Fuzzy Syst 10(5):607–621

    Article  Google Scholar 

  20. Oh SK, Pedrycz W, Park BJ (2003) Self-organizing neurofuzzy networks based on evolutionary fuzzy granulation. IEEE Trans Syst Man and Cybern A 33(2):271–277

    MathSciNet  Google Scholar 

  21. Cordon O et al. (2004) Ten years of genetic fuzzy systems: current framework and new trends. Fuzzy Sets Syst 141(1):5–31

    Article  MATH  MathSciNet  Google Scholar 

  22. Ivahnenko AG (1968) The group method of data handling: a rival of method of stochastic approximation. Sov Autom Control 13(3):43–55

    Google Scholar 

  23. Yamakawa T (1993) A new effective learning algorithm for a neo fuzzy neuron model. 5th IFSA World Conference:1017–1020

    Google Scholar 

  24. Oh SK, Yoon KC, Kim HK (2000) The Design of optimal fuzzy- eural networks structure by means of GA and an aggregate weighted performance index. J Control, Autom Syst Eng 6(3):273–283 (In Korean)

    Google Scholar 

  25. Park MY, Choi HS (1990) Fuzzy control system. Daeyoungsa, Seoul (In Korean)

    Google Scholar 

  26. Box G.EP, Jenkins GM (1976) Time series analysis, forecasting, and control, 2nd edn. Holden-Day, SanFransisco

    MATH  Google Scholar 

  27. Ahn TC, Oh SK (1997) Intelligent models concerning the pattern of an air pollutant emission in a thermal power plant, Final Report, EESRI

    Google Scholar 

  28. Kondo T (1986) Revised GMDH algorithm estimating degree of the complete polynomial. Trans Soc Instrum Control Eng 22(9):928–934

    Google Scholar 

  29. Park HS, Oh SK (2003) Multi-FNN identification based on HCM clustering and evolutionary fuzzy granulation. Int J Control, Autom Syst 1(2):194–202

    Google Scholar 

  30. Kim E, Lee H, Park M, Park M (1998) A simply identified sugeno-type fuzzy model via double clustering. Inf Sci 110:25–39

    Article  Google Scholar 

  31. Lin Y, Cunningham III GA (1997) A new approach to fuzzy-neural modeling, IEEE Trans Fuzzy Syst 3(2):190–197

    Google Scholar 

  32. Oh SK, Pedrycz W, Park HS (2003) Hybrid identification in fuzzy-neural networks. Fuzzy Sets Syst 138(2):399–426

    Article  MathSciNet  Google Scholar 

  33. Park HS, Oh SK (2000) Multi-FNN identification by means of HCM clustering and its optimization using genetic algorithms. J Fuzzy Logic Intell Syst 10(5):487–496 (In Korean)

    Google Scholar 

  34. Park BJ, Oh SK, Jang SW (2002) The design of adaptive fuzzy polynomial neural networks architectures based on fuzzy neural networks and self-organizing networks. J Control Autom Syst Eng 8(2):126–135 (In Korean)

    Google Scholar 

  35. Park BJ, Oh SK (2002) The analysis and design of advanced neurofuzzy polynomial networks. J Inst Electron Eng Korea 39-CI(3):18–31 (In Korean)

    Google Scholar 

  36. Park BJ, Oh SK, Pedrycz W, Kim HK (2005) Design of evolutionally optimized rule-based fuzzy neural networks on fuzzy relation and evolutionary optimization. International Conference on Computational Science. Lecture Notes in Computer Science 3516:1100–1103

    Google Scholar 

  37. Oh SK, Park BJ, Pedrycz W, Kim HK (2005) Evolutionally optimized fuzzy neural networks based on evolutionary fuzzy granulation. Lecture Notes in Computer Science 3483:887–895

    Google Scholar 

  38. Oh SK, Park BJ, Pedrycz W, Kim HK (2005) Genetically optimized hybrid fuzzy neural networks in modeling software data. Lecture Notes in Artificial Intelligence 3558:338–345

    Google Scholar 

  39. Zadeh NN, Darvizeh A, Jamali A, Moeini A (2005) Evolutionary design of generalized polynomial neural networks for modeling and prediction of explosive forming process. J Mater Process Technol 164(15):1561–1571

    Google Scholar 

  40. Delivopoulos E, Theocharis JB (2004) A modified PNN algorithm with optimal PD modeling using the orthogonal least squares method. Inf Sci 168(3):133–170

    MATH  MathSciNet  Google Scholar 

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Quintero, A., Pierre, S. (2008). On the Design of Large-scale Cellular Mobile Networks Using Multi-population Memetic Algorithms. In: Abraham, A., Grosan, C., Pedrycz, W. (eds) Engineering Evolutionary Intelligent Systems. Studies in Computational Intelligence, vol 82. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75396-4_13

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  • DOI: https://doi.org/10.1007/978-3-540-75396-4_13

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-75395-7

  • Online ISBN: 978-3-540-75396-4

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