Skip to main content
SpringerLink
Log in

Network Design Techniques Using Adapted Genetic Algorithms

  • Conference paper
Evolutionary Design and Manufacture

Abstract

In recent years we have evidenced an extensive effort in the development of computer communication networks, which have deeply integrated in human being’s everyday life. One of the important aspects of the network design process is the topological design problem involved in establishing a communication network. However, with the increase of the problem scale, the conventional techniques are facing the challenge to effectively and efficiently solve those complicated network design problems. In this article, we give out our recent research works on the network design problems by using genetic algorithms (GAs), such as, multistage process planning problem, fixed charge transportation problem, minimum spanning tree problem, centralized network design, and local area network design. All these problems are illustrated from the point of genetic representation encoding skill and the genetic operators with hybrid strategies. Large quantities of numerical experiments show the effectiveness and efficiency of such kind of GA-based approach.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Awadh, B., Sepehri, N., and Hawaleshka, O. (1995) A Computer-aided Process Planning Model based on Genetic Algorithms, Computers & Operations Research, 22 841–856.

    Article  MATH  Google Scholar 

  2. Bertsekas, D. and Gallager, R. (1992) Data Networks, 2nd ed., Prentice-Hall, New Jersey.

    MATH  Google Scholar 

  3. Chang, T. C. and Wysk, R. A. (1985) An Introduction to Automated Process Planning Systems, Prentice-Hall, Englewood Cliffs.

    Google Scholar 

  4. Cheng, R. (1997) Study on Genetic Algorithm-based Optimal Scheduling Techniques, PhD dissertation, Tokyo Institute of Technology, Japan.

    Google Scholar 

  5. Cheng, R. and Gen, M. (1998) An Evolution Program for the Resource-Constrained Project Scheduling Problem, Computer Integrated Manufacturing, 11 (3) 274–287.

    Article  Google Scholar 

  6. Cooper, L. (1963) Location-allocation problems, Operations Research, 11 (3) 331–344.

    Article  MathSciNet  MATH  Google Scholar 

  7. Cormen, T.H., Leiserson, C.E., & Rivest, R.L. (1990) Introduction to Algorithms, The MTT Press.

    MATH  Google Scholar 

  8. Domschke, K. and Drex, A. (1984) An International Bibliography on Location and Layout Planning, Springer, Heidelberg.

    Google Scholar 

  9. Gavish, B. (1982) Topological Design of Centralized Computer Networks-Formulation and Algorithms, Networks, 12 355–377.

    Article  MathSciNet  MATH  Google Scholar 

  10. Gavish, B. (1985) Augmented Lagrangian-based Algorithms for Centralized Network Design, IEEE transaction on Commun., COM-33 1247–1257.

    Article  MathSciNet  Google Scholar 

  11. Gen, M. and Cheng, R. (1997) Genetic Algorithms and Engineering Design, John & Wiley Sons, New York.

    Google Scholar 

  12. Gen, M. and Cheng, R. (2000) Genetic Algorithms and Engineering Optimization, John & Wiley Sons, New York.

    Google Scholar 

  13. Gen, M., Cheng, W., and Wang, D. (1997) Genetic Algorithms for Solving Shortest Path Problems, Proceedings of IEEE International Conference on Evolutionary Computation, Indianapolis, Indiana, 401–406.

    Google Scholar 

  14. Gen, M., Ida, K. and Kim, J. R. (1998) A Spanning Tree-based Genetic Algorithm for Bicriteria Topological Network Design, Proceedings of IEEE International Conference on Evolutionary Computation, Anchorage, Alaska, 15–20.

    Google Scholar 

  15. Gen, M. and Kim, J. R. (1998) GA-based Optimal Network Design: A State-of-the-Art Survey, in Dagli, C. H., et al eds. Intelligent Engineering Systems, Through Artifical Neural Networks, 8 247–252, ASME Press, New York.

    Google Scholar 

  16. Gen, M. and Li, Y. (1998) Solving Multiobjective Transportation Problem by Spanning Tree-based Genetic Algorithm, in L Parmee ed., Adaptive Computing in Design and Manufacture, Springer-Verlag, 98–108.

    Google Scholar 

  17. Gen, M. and Li Y. Z. (1999) Hybrid Genetic Algorithms for Transportation Problem in Logistics, Journal of Logistics; Research & Applications (forthcoming).

    Google Scholar 

  18. Goldberg, D.E. (1989) Genetic Algorithms in Search, Optimisation and Machine Learning, Addison-Wesley, Reading.

    Google Scholar 

  19. Gong, D., Gen, M., Yamazaki, G., & Xu, W. (1995) Hybrid Evolutionary Method for Obstacle Location-Allocation Problem, Computers and Industrial Engineering, 29 (1–4) 525–530.

    Article  Google Scholar 

  20. Gong, D., Gen, M., Yamazaki, G., & Xu, W. (1997) Hybrid Evolutionary Method for Capacitated Location-Allocation, Engineering Design & Automation, 3 (2) 166–173.

    Google Scholar 

  21. Gottlieb, J. & Paulmann, L. (1998) Genetic Algorithms for the Fixed Charge Transportation Problem, Proceedings of IEEE International Conference on Evolutionary Computation, Anchorage, Alaska, 330–335.

    Google Scholar 

  22. Gouveia, L. (1995) A 2n Constraint Formulation for the Capacitated Spanning Tree Problem, Operations Research, 43 (1) 130–141.

    Article  MathSciNet  MATH  Google Scholar 

  23. Hall, L. (1996) Experience with a Cutting Plane Algorithm for the Capacitated Minimal Spanning Tree Problem, INFORMS Journal on Computing, 8 (3) 219–234.

    Article  MATH  Google Scholar 

  24. Holland, J. H. (1975) Adaptation in Natural And Artificial Systems, MIT Press, Cambridge, MA.

    Google Scholar 

  25. Hwang, C. and Yoon, K. (1981) Multiple Attribute Decision-Making: Methods and Applications, Springer-Verlag.

    MATH  Google Scholar 

  26. Jensen, A. P. and Barnes, J. W. (1980) Network Flow Programming, John Wiley, New York.

    MATH  Google Scholar 

  27. Katz, I. and Cooper, L. (1981) Facility Location in the Presence of Forbidden Regions; I. Formulation and the Case of the Euclidean Distance with one Forbidden Circle, European Journal of Operational Research, 6166–173.

    Article  MathSciNet  MATH  Google Scholar 

  28. Kim, J. R., Gen, M. and Ida, K. (1999) Bicriteria Network Design using Spanning Tree-based Genetic Algorithm, Artificial Life and Robotics, 365–72.

    Article  Google Scholar 

  29. Kim, J. R., Gen, M., and Yamashiro, M. (1999) A Bi-level Hierarchical GA for Reliable Network Topology Design, Proceedings of the 7th European Congress on Intelligent Techniques & Soft Computing, Session CD-7, Aachen.

    Google Scholar 

  30. Kusiak, A. and Finke, G. (1988) Selection of Process Plans in Automated Manufacturing Systems, IEEE Journal of Robotics and Automation, 4(4), 397–402.

    Article  Google Scholar 

  31. Li, Y. Z. and Gen, M. (1997) Spanning Tree-based Genetic Algorithm for Bicriteria Transportation Problem with Fuzzy Coefficients, Australian J. of Intelligent Inform. Processing Systems, 4 (3/4) 220–229.

    Google Scholar 

  32. Li, Y. Z., Gen, M., and Ida, Y. (1998) Fixed Charge Transportation Problem by Spanning Tree-based Genetic Algorithm, Beijing Mathematics, 4(2) 239–249.

    Google Scholar 

  33. Li, Y. Z. (1999) Study on Hybridized Genetic Algorithm for Production Distribution Planning Problems, PhD dissertation, Ashikaga Institute of Technology, Japan.

    Google Scholar 

  34. Malik, K. and Yu, G. (1993) A Branch and Bound Algorithm for the Capacitated Minimum Spanning Tree Problem, Networks, 23525–532.

    Article  MathSciNet  MATH  Google Scholar 

  35. McHugh, J. A. (1990) Algorithmic Graph Theory, Prentice Hall, New Jersey.

    MATH  Google Scholar 

  36. Multagh, B. and Niwattisyawong, S. (1984) An Efficient Method for the Muti-depot Location-Allocation Problem, Journal of Operational Research Society, 33629–634.

    Google Scholar 

  37. Narula, S. C. and Ho, C. A. (1980) Degree-constrained Minimum Spanning Tree. Computers & Operations Research, 7239–249.

    Article  Google Scholar 

  38. Prüfer, H. (1918) Neuer beweis eines Satzes über Permutationen, Arch. Math. Phys, 27742–744.

    Google Scholar 

  39. Rosing, K. (1992) An Optimal Method for Solving (Generalized) Multi-Weber Problem, European Operational Research, 58, 479–486.

    Google Scholar 

  40. Sancho, N. G. (1986) A Multi-objective Routing Problem, Engineering Optimization, 10, 71–76.

    Article  Google Scholar 

  41. Sniedovich, M. (1988) A Multi-objective Routing Problem Revisited, Engineering Optimisation, 13, 99–108.

    Article  Google Scholar 

  42. Sun, M., Aronson, J. E. Mckeown, P. G. and Drinka, D. (1998) A Tabu Search Heuristic Procedure for the Fixed Charge Transportation Problem, European J. of Operational Research, 106441–456.

    Article  MATH  Google Scholar 

  43. Vignaux, G. A. and Michalewicz, Z. (1991) A Genetic Algorithm for the Linear Transportation Problem, IEEE Transactions on Systems, Man, and Cybernetics, 21445–452.

    Article  MathSciNet  MATH  Google Scholar 

  44. Walters, G. A. and Smith, D. K. (1995) Evolutionary Design Algorithm for Optimal Layout of Tree Networks, Engineering Optimisation, 24261–281.

    Article  Google Scholar 

  45. Whatley, J. K. (1985) SAS/OR User’s Guide: Version 5. Netflow Procedure, SAS Institute Inc., Cary, NC, 211–223.

    Google Scholar 

  46. Zhou, G. and Gen, M. (1997a) Evolutionary Computation on Multicriteria Production Process Planning Problem, in B. Porto editor, Proceedings of the IEEE International Conference on Evolutionary Computation, 419–424.

    Google Scholar 

  47. Zhou, G. and Gen, M. (1997b) A Note on Genetic Algorithm Approach to the Degree-Constrained Spanning Tree Problems, Networks, 30105–109.

    Article  Google Scholar 

  48. Zhou, G. and Gen, M. (1997c) Approach to Degree-Constrained Minimum Spanning Tree Problem Using Genetic Algorithm, Engineering Design and Automation, 3(2) 157–165.

    Google Scholar 

  49. Zhou, G. and Gen, M. (1999) A New Tree Encoding for the Degree-Constrained Spanning Tree Problem, Soft Computing (forthcoming)

    Google Scholar 

  50. Zhou, G. (1999) Study on Constrained Spanning Tree Problems with Genetic Algorithms, PhD dissertation, Ashikaga Institute of Technology, Japan.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Dept of Industrial Engg. and Operations Res., University of California, Berkeley, CA, 94720, USA

    Mitsuo Gen

  2. Department of Industrial and Information Systems Engineering, Ashikaga Institute of Technology, Ashikaga, 326-8558, Japan

    Mitsuo Gen & Runwei Cheng

  3. Department of Industrial Engineering and Operations Research, University of California, Berkeley, CA, 94720, USA

    Shmuel S. Oren

Authors
  1. Mitsuo Gen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Runwei Cheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shmuel S. Oren
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Engineering Design Centre, University of Plymouth, Drake Circus, Plymouth Devon, PL4 8AA, UK

    I. C. Parmee BSc, PhD

Rights and permissions

Reprints and permissions

Copyright information

© 2000 Springer-Verlag London

About this paper

Cite this paper

Gen, M., Cheng, R., Oren, S.S. (2000). Network Design Techniques Using Adapted Genetic Algorithms. In: Parmee, I.C. (eds) Evolutionary Design and Manufacture. Springer, London. https://doi.org/10.1007/978-1-4471-0519-0_9

Download citation

  • DOI: https://doi.org/10.1007/978-1-4471-0519-0_9

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-85233-300-3

  • Online ISBN: 978-1-4471-0519-0

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation