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A Hybrid Estimation of Distribution Algorithm for CDMA Cellular System Design

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Simulated Evolution and Learning (SEAL 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4247))

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Abstract

While code division multiple access (CDMA) is becoming a promising cellular communication system, the design for a CDMA cellular system configuration has posed a practical challenge in optimisation. The study in this paper proposes a hybrid estimation of distribution algorithm (HyEDA) to optimize the design of a cellular system configuration. HyEDA is a two-stage hybrid approach built on estimation of distribution algorithms (EDAs), coupled with a K-means clustering method and a simple local search algorithm. Compared with the simulated annealing method on some test instances, HyEDA has demonstrated its superiority in terms of both the overall performance in optimisation and the number of fitness evaluations required.

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References

  1. Baluja, S.: Population-based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning, CMU-CS-94-163, Carnegie Mellon University, Pittersburgh, PA (1994)

    Google Scholar 

  2. Mühlenbein, H., Paaß, G.: From Recombination of Gens to the Estimation of Distribution I: Binary Parameters. In: Ebeling, W., Rechenberg, I., Voigt, H.-M., Schwefel, H.-P. (eds.) PPSN 1996. LNCS, vol. 1141, pp. 178–187. Springer, Heidelberg (1996)

    Chapter  Google Scholar 

  3. Garg, V.K., Smolik, K., Wilkes, J.E.: Applications of CDMA in Wireless/Personal Communications. Prentice-Hall, Upper Saddle River (1997)

    Google Scholar 

  4. Pelikan, M., Goldberg, D.E.: Genetic Algorithms, Clustering, and the Breaking of Symmetry, Illinois Genetic Algorithm Laboratory, University of Illinois at Urbana-Champaign, Urbana, IL (2000)

    Google Scholar 

  5. Sun, J.: Hybrid Estimation of Distribution Algorithms for Optimization Problems, PhD thesis, University of Essex (2005)

    Google Scholar 

  6. Sun, J., Zhang, Q., Tsang, E.P.K.: DE/EDA: A New Evolutionary Algorithm for Global Optimization. Information Sciences 169(3), 249–262 (2005)

    Article  MathSciNet  Google Scholar 

  7. Kim, K.I.: Handbook of CDMA System Design, Engineering and Optimisation. Prentice-Hall, Englewood Cliffs (1999)

    Google Scholar 

  8. Krasnogor, N., Hart, W., Smith, J.: Recent Advances in Memetic Algorithms and Related Search Technologies. Springer (to appear, 2003)

    Google Scholar 

  9. Larraanaga, P., Lozano, J.A.: Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation. Kluwer Academic Publishers, Norwell (2001)

    Google Scholar 

  10. Li, J., Taiwo, S.: Enhancing Financial Decision Making Using Multi-Objective Financial Genetic Programming. In: Proceeding of 2006 IEEE Congress on Evolutionary Computation (CEC 2006), July 16-21, 2006 Sheraton Vancouver Wall Centre, Vancouver, BC, Canada (to appear, 2006)

    Google Scholar 

  11. Lu, Q., Yao, X.: Clustering and Learning Gaussian Distribution for Continuous Optimisation. IEEE Transcations on Systems, Man, and Cybernetics, Part C 35(2), 195–204 (2005)

    Article  Google Scholar 

  12. Melachrinoudis, E., Rosyidi, B.: Optimizing the Design of a CDMA Cellular System Configuration with Multiple Criteria. Annals of Operations Research 106, 307–329 (2001)

    Article  MATH  MathSciNet  Google Scholar 

  13. Salcedo-Sanz, S., Xu, Y., Yao, X.: Hybrid Meta-Heuristic Algorithms for Task Assignment in Heterogeneous Computing Systems. Computers and Operations Research 33(3), 820–835 (2006)

    Article  MATH  Google Scholar 

  14. Salcedo-Sanz, S., Yao, X.: A Hybrid Hopfield Network – Genetic Algorithm Approach for the Terminal Assignment Problem. IEEE Transactions on System, Man and Cybernetics, Part B: Cybernetics 34(6), 2343–2353 (2004)

    Article  Google Scholar 

  15. Zhang, Q., Sun, J., Tsang, E.P.K., Ford, J.A.: Hybrid estimation of distribution algorithm for global optimisation. Engineering Computations 21(1), 91–107 (2003)

    Article  Google Scholar 

  16. Zhang, Q., Sun, J., Xiao, G., Tsang, E.: Evolutionary Algorithm Refining a Heuristic: A Hybrid Method for Shared Path Protection in WDM Networks under SRLG Constraints. IEEE Transactions on System, Man and Cybernetics (to appear, 2006)

    Google Scholar 

  17. Zhang, Q., Sun, J., Tsang, E.P.K.: Evolutionary Algorithm with the Guided Mutation for the Maximum Clique Problem. IEEE Transactions on Evolutionary Computation 9(2), 192–200 (2005)

    Article  Google Scholar 

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© 2006 Springer-Verlag Berlin Heidelberg

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Sun, J., Zhang, Q., Li, J., Yao, X. (2006). A Hybrid Estimation of Distribution Algorithm for CDMA Cellular System Design. In: Wang, TD., et al. Simulated Evolution and Learning. SEAL 2006. Lecture Notes in Computer Science, vol 4247. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11903697_114

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  • DOI: https://doi.org/10.1007/11903697_114

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-47331-2

  • Online ISBN: 978-3-540-47332-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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