Abstract
This paper presents a Nash genetic algorithm (Nash GA) as a solution for a network design problem, formulated as a bi-level programming model and designs a backbone topology in a hierarchical Link-State (LS) routing domain. Given that the sound backbone topology structure has a great impact on the overall routing performance in a hierarchical LS domain, the importance of this research is evident. The proposed decision model will find an optimal configuration that consists of backbone router for Backbone Provider (BP), router for Internet Service Provider (ISP), and connection link properly meeting two-pronged engineering goals: i.e., average message delay and connection costs. It is also presumed that there are decision makers for BP and the decision makers for ISP join in the decision making process in order to optimize the own objective function. The experiment results clearly indicates that it is essential to the effective operations of hierarchical LS routing domain to consider not only the engineering aspects but also specific benefits from systematical layout of backbone network, which presents the validity of the decision model and Nash GA.
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References
Moy, J.T.: OSPF - Anatomy of an Internet Routing Protocol. Addison Wesley, Reading (1998)
Parkhurst, W.R.: Cisco OSPF Command and Configuration Handbook. Cisco Press (2002)
Thomas, T., Thomas II, M.: OSPF Network Design Solutions. Cisco Press (2003)
Mukherjee, B.: Optical Communication Networks. McGraw-Hill, Inc., New York (1997)
Gen, M., Cheng, R.: Genetic Algorithms and Engineering Design. John Wiley & Sons, New York (1997)
Gen, M., Cheng, R.: Genetic Algorithms and Engineering Optimization. John Wiley & Sons, New York (2000)
Lim, Y.T., Lim, K.W.: Game Theory and Traffic Network Problems. The Journal of Environmental Studies 42, 107–121 (2004) (in Korean)
Kim, D.H., Hyun, C.H.: Game Structure of Info-telecommunication Policy and its Dynamics. The Journal of National Basic Information System 2(4), 36–48 (1995) (in Korean)
Yun, K.L.: A Game Theoretic Analysis of the Interconnection Pricing in Network Industries, KAIST Graduate School of Management, Dissertation (1997)
Wang, J.F., Periaux, J.: Multi-point Optimization using GAs and Nash/Stackelberg Games for High Lift Multi-airfoil Design in Aerodynamics. In: Proc. of the 2001 Congress on Evolutionary Computation, vol. 1, pp. 552–559 (2001)
Sim, K.B., Ki, J.Y., Lee, D.W.: Optimization of Multi-objective Function based on the Game Theory and Co-evolutionary Algorithm. Journal of Fuzzy Logic and Intelligent Systems 12(6), 491–496 (2002) (in Korean)
Sefrioui, M., Periaux, J.: Nash Genetic Algorithms: Examples and Applications. In: Proc. of the 2000 Congress on Evolutionary Computation, pp. 509–516 (2000)
Bertsekas, D., Gallager, R.: Data Networks, 2nd edn. Prentice-Hall, New Jersey (1992)
Elbaum, R., Sidi, M.: Topological Design of Local-area Networks using Genetic Algorithms. IEEE/ACM Transactions on Networking 4(5), 766–778 (1996)
Kershenbaum, A., Van Slyke, R.: Recursive Analysis of Network Reliability. Networks 3, 81–94 (1973)
Raidl, G.R., Julstrom, B.A.: Edge Sets: an Effective Evolutionary Coding of Spanning Trees. IEEE Transaction on Evolutionary Computation 7(3), 225–239 (2003)
Gen, M., Cheng, R.: Evolutionary Network Design: Hybrid Genetic Algorithms Approach. International Journal of Computational Intelligence and Applications 3(4), 357–380 (2003)
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Kim, J.R., Jo, J.B., Yang, H.K. (2007). A Solution for Bi-level Network Design Problem Through Nash Genetic Algorithm. In: Szczuka, M.S., et al. Advances in Hybrid Information Technology. ICHIT 2006. Lecture Notes in Computer Science(), vol 4413. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77368-9_27
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DOI: https://doi.org/10.1007/978-3-540-77368-9_27
Publisher Name: Springer, Berlin, Heidelberg
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