A Hybrid Estimation of Distribution Algorithm for CDMA Cellular System Design

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.

[1]  Kyoung Il Kim Handbook of CDMA System Design, Engineering, and Optimization , 1999 .

[2]  Pedro Larrañaga,et al.  Towards a New Evolutionary Computation - Advances in the Estimation of Distribution Algorithms , 2006, Towards a New Evolutionary Computation.

[3]  David E. Goldberg,et al.  Genetic Algorithms, Clustering, and the Breaking of Symmetry , 2000, PPSN.

[4]  Mitsuo Gen,et al.  Genetic algorithms and engineering optimization , 1999 .

[5]  Qingfu Zhang,et al.  Combination of Guided Local Search and Estimation of Distribution Algorithm for Quadratic Assignment Problems , 2006 .

[6]  Anil K. Jain,et al.  Algorithms for Clustering Data , 1988 .

[7]  Heinz Mühlenbein,et al.  Schemata, Distributions and Graphical Models in Evolutionary Optimization , 1999, J. Heuristics.

[8]  H. Mühlenbein,et al.  From Recombination of Genes to the Estimation of Distributions I. Binary Parameters , 1996, PPSN.

[9]  Qingfu Zhang,et al.  An evolutionary algorithm with guided mutation for the maximum clique problem , 2005, IEEE Transactions on Evolutionary Computation.

[10]  Jin Li,et al.  Enhancing Financial Decision Making Using Multi-Objective Financial Genetic Programming , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[11]  Roger M. Whitaker,et al.  Comparison and Evaluation of Multiple Objective Genetic Algorithms for the Antenna Placement Problem , 2005, Mob. Networks Appl..

[12]  Andrew R Nix,et al.  The automatic location of base-stations for optimised cellular coverage: a new combinatorial approach , 1999, 1999 IEEE 49th Vehicular Technology Conference (Cat. No.99CH36363).

[13]  J. A. Lozano,et al.  Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation , 2001 .

[14]  Rudolf Mathar,et al.  Optimum positioning of base stations for cellular radio networks , 2000, Wirel. Networks.

[15]  Qingfu Zhang,et al.  Evolutionary Algorithms Refining a Heuristic: A Hybrid Method for Shared-Path Protections in WDM Networks Under SRLG Constraints , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[16]  Dr. Zbigniew Michalewicz,et al.  How to Solve It: Modern Heuristics , 2004 .

[17]  Christian Blum,et al.  Metaheuristics in combinatorial optimization: Overview and conceptual comparison , 2003, CSUR.

[18]  Emanuel Melachrinoudis,et al.  Optimizing the Design of a CDMA Cellular System Configuration with Multiple Criteria , 2001, Ann. Oper. Res..

[19]  Qingfu Zhang,et al.  DE/EDA: A new evolutionary algorithm for global optimization , 2005, Inf. Sci..

[20]  Xin Yao,et al.  Hybrid meta-heuristics algorithms for task assignment in heterogeneous computing systems , 2006, Comput. Oper. Res..

[21]  Kenneth Frank Smolik,et al.  Applications Of Cdma In Wireless/Personal Communications , 1996 .

[22]  Shumeet Baluja,et al.  A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning , 1994 .

[23]  Gary B. Lamont,et al.  Applications Of Multi-Objective Evolutionary Algorithms , 2004 .

[24]  Xin Yao,et al.  Clustering and learning Gaussian distribution for continuous optimization , 2005, IEEE Trans. Syst. Man Cybern. Part C.

[25]  J. Ford,et al.  Hybrid estimation of distribution algorithm for global optimization , 2004 .

[26]  Xin Yao,et al.  A hybrid Hopfield network-genetic algorithm approach for the terminal assignment problem , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).