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A Simulated Annealing Approach to Communication Network Design

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Abstract

This paper explores the use of the meta-heuristic search algorithm Simulated Annealing for solving a minimum cost network synthesis problem. This problem is a common one in the design of telecommunication networks. The formulation we use models a number of practical problems with hop-limit, degree and capacity constraints. Emphasis is placed on a new approach that uses a knapsack polytope to select amongst a number of pre-computed traffic routes in order to synthesise the network. The advantage of this approach is that a subset of the best routes can be used instead of the whole set, thereby making the process of designing large networks practicable. Using simulated annealing, we solve moderately large networks (up to 30 nodes) efficiently.

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References

  • D. Abramson and M. Randall, “A simulated annealing code for general integer linear programs,” Annals of Operations Research, vol. 86, pp. 3–21,1999.

    Google Scholar 

  • A. Balakrishnan, T. Magnanti, A. Shulman, and R. Wong, “Models for planning capacity expansion in local access telecommunication networks,”Annals of Operations Research, vol. 33, pp.239–284,1991.

    Google Scholar 

  • L. Berry, B. Murtagh, S. Sugden, and G. McMahon, “Application of a genetic-based algorithm for optimal design of tree-structured communications networks,” in Proceedings of the Regional Teletraffic Engineering Conference of the International Teletraffic Congress, South Africa, 1995, pp. 361–370.

  • L. Berry, B. Murtagh, S. Sugden, and G. McMahon, “An integrated GA-LP approach to communication network design,” in Proceedings of the 2nd IFIP Workshop on Traffic Management and Synthesis of ATM Networks, Canada, 1997.

  • N. Collins, R. Eglese, and B. Golden, “Simulated annealing: An annotated bibliography,” American Journal of Mathematical and Management Sciences, vol. 8, pp. 209–307, 1988.

    Google Scholar 

  • D. Connolly, “An improved annealing for the QAP,” European Journal of Operational Research, vol. 46, pp. 93–100, 1990.

    Google Scholar 

  • R. Eglese, “Simulated annealing: A tool for operational research,” European Journal of Operational Research,vol. 46, pp. 271–281,1990.

    Google Scholar 

  • B. Gavish, “Augmented Lagrangian based algorithms for centralised network design,” IEEE Transactions on Communications, vol. 33, pp.1247–1257,1985.

    Google Scholar 

  • D. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning, AddisonWesley: Reading, MA, 1989.

    Google Scholar 

  • A. Kershenbaum and S. Peng, “Neighbor finding algorithms for CMST customer calculations,” IEEE INFOCOM, Miami, Florida,1986.

    Google Scholar 

  • S. Kirkpatrick, D. Gelatt, and M. Vecchi, “Optimization by simulated annealing,” Science, vol.220, pp. 671–680, 1983.

    Google Scholar 

  • C. Koulamas, S. Antony, and R. Jansen, “A survey of simulated annealing to operations research problems,” Omega International Journal of Management Science, vol. 22, pp. 41–56, 1994.

    Google Scholar 

  • T. Magnanti and S. Raghavan, “Strong formulations for network design problems with connectivity requirements,” TR 99-28, Institute for Systems Research, University of Maryland, 1999.

  • G. McMahon, S. Sugden, L. Berry, B. Murtagh, and M. Randall, “Fast network design for telecommunications,” TR99-11, School of Information Technology, Bond University, 1999.

  • M. Minoux, “Network synthesis and the optimum network design problem: Models, solution methods and applications,” Networks, vol.19, pp. 313–360,1989.

    Google Scholar 

  • I. Osman and J. Kelly, Metaheuristics: Theory and Applications, Kluwer Academic Publishers: Norwell, MA, 1996.

    Google Scholar 

  • C. Palmer and A. Kershenbaum, “An approach to a problem in network design using genetic algorithms,” Networks, vol.26, pp.151–163,1995.

    Google Scholar 

  • C. Petersen, “Computational experience of the balas algorithm applied to a selection of R&D projects,” Management Science, vol. 13, pp.736–750,1967.

    Google Scholar 

  • M. Randall and D. Abramson, “A general purpose meta-heuristic based solver for combinatorial optimisation problems,” Journal of Computational Optimization and Applications, vol.20, pp. 185–210,2001.

    Google Scholar 

  • U. Sharma, K. Mistra, and A. Bhattacharji, “Applications of efficient search techniques for optimal design of computer communication networks,” Micro Electronics and Reliability, vol. 31, pp.337–341,1991.

    Google Scholar 

  • L. van Laarhoven and E. Aarts, Simulated Annealing: Theory and Applications, D Reidel Publishing Company: Dordecht,1987.

    Google Scholar 

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Randall, M., McMahon, G. & Sugden, S. A Simulated Annealing Approach to Communication Network Design. Journal of Combinatorial Optimization 6, 55–65 (2002). https://doi.org/10.1023/A:1013337324030

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