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An integrated GA–LP approach to communication network design

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Abstract

In this paper we demonstrate success with an implementation of a genetic algorithm, integrated with linear programming, for solving a minimum cost network synthesis problem. The problem is formulated to include a number of practical constraints and the technique applied to moderately large networks (50 nodes). The associated linear program may be large but successful methods have been developed with very small population sizes for the genetic algorithm.

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References

  1. C.C. Aggarwal, J.B. Orlin and R.P. Tai, Optimized crossover for the independent set problem, Operations Research 45 (1997) 226–234.

    Article  Google Scholar 

  2. A. Balakrishnan, T.L. Magnanti, A. Shulman and R.T. Wong, Models for planning capacity expansion in local access telecommunication networks, Annals of Operations Research 33 (1991) 239–284.

    Article  Google Scholar 

  3. L.T.M. Berry, Network structures for economic communication, in: ITC 13 Teletraffic and Datatraf-fic in a Period of Change, North Holland Studies in Telecommunications, Vol. 14 (1991) pp. 213–218.

    Google Scholar 

  4. L.T.M. Berry, B.A. Murtagh, S.J. Sugden and G.B. McMahon, Application of a genetic-based algorithm for optimal design of tree-structured communications networks, in: Proc.of the Regional Teletraffic Engineering Conference of the International Teletraffic Congress, South Africa (1995) 361–370.

  5. R. Elbaum and M. Sidi, Topological design of local-area networks using genetic algorithms, IEEE/ACM Transactions on Networking 4(5) (1996) 766–778.

    Article  Google Scholar 

  6. B. Gavish, Augmented Lagrangian based algorithms for centralised network design, IEEE Transactions on Communications 33 (1985) 1247–1257.

    Article  Google Scholar 

  7. D.E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning (Addison-Wesley, Reading, MA, 1989).

    Google Scholar 

  8. R. Guerin, H. Ahmadi and M. Nagshineh, Equivalent capacity and its application to bandwidth allocation in high speed networks, IEEE Journal on Selected Areas in Communications 9 (1991) 968–981.

    Article  Google Scholar 

  9. J.H. Holland, Adaptation in Natural and Artificial Systems (Univ. of Michigan Press, Ann Arbor, 1975) (Reprinted by MIT Press, Cambridge, MA, 1992).

    Google Scholar 

  10. K. De Jong and W. Spears, On the state of evolutionary computation, in: Proc.of the 1993 Internat. Conf.on Genetic Algorithms, Urbana-Champaign, IL (1993) pp. 618–623.

  11. B.A. Julstrom, A simple estimate of population size in genetic algorithms for the travelling salesman problem, in: Proc.of the 2nd Nordic Workshop on Genetic Algorithms and Their Applications, Vaasa, Finland (1996) 3–13.

  12. A. Kershenbaum and S.L. Peng, Neighbour finding algorithms for CMST customer calculations, in: IEEE INFOCOM (1986).

  13. A. Kumar, R.M. Pathak and Y.P. Gupta, Genetic-algorithm-based reliability optimization for computer network expansion, IEEE Transactions on Reliability 44(1) (1995) 63–72.

    Article  Google Scholar 

  14. S.W. Mafoud, Population size and genetic drift in fitness sharing, in: Foundations of Genetic Algorithms, eds. L.D. Whitely and M.D. Vose (San Francisco, 1995) pp. 185–223.

  15. M.L. Mauldin, Maintaining diversity in genetic search, in: Proc.of the National Conf.on Artificial Intelligence (1984) pp. 247–250.

  16. M. Minoux, Network synthesis and optimum network design problems: Models, solution methods and applications, Networks 19 (1989) 313–360.

    Google Scholar 

  17. B.A. Murtagh and S.J. Sugden, A direct search approach to nonlinear integer programming, Optimization Methods and Software 4 (1994) 171–189.

    Google Scholar 

  18. C.C. Palmer, An approach to a problem in network design using genetic algorithms, Networks 26 (1995) 151–163.

    Google Scholar 

  19. S. Pierre and G. Legault, An evolutionary approach for configuring economical packet switched computer networks, Artificial Intelligence in Engineering 10 (1996) 127–134.

    Article  Google Scholar 

  20. U. Sharma, K.B. Misra and A.K. Bhattacharji, Applications of an efficient search technique for optimal design of computer communication networks, Micro Electronics & Reliability 31 (1991) 337–341.

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Systems Engineering, RMIT University, GPO Box 2476V, Melbourne, Victoria, 3001, Australia

    L.T.M. Berry & L. Welling

  2. Graduate School of Management, Macquarie University, North Ryde, N.S.W, 2109, Australia

    B.A. Murtagh

  3. School of Information Technology, Bond University, Gold Coast, Queensland, 4299, Australia

    G. McMahon & S. Sugden

Authors
  1. L.T.M. Berry
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  2. B.A. Murtagh
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  3. G. McMahon
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  4. S. Sugden
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  5. L. Welling
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Berry, L., Murtagh, B., McMahon, G. et al. An integrated GA–LP approach to communication network design. Telecommunication Systems 12, 265–280 (1999). https://doi.org/10.1023/A:1019102930443

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