Skip to main content
SpringerLink
Log in

A survivable design of last mile communication networks using multi-objective genetic algorithms

  • Regular Research Paper
  • Published:
Memetic Computing Aims and scope Submit manuscript

Abstract

In this paper, we are interested in the survivable network design problem (SNDP) for last mile communication networks called (L-SNDP). Given a connected, weighted, undirected graph \(\mathrm{{G}} = (\mathrm{V, E})\); a set of infrastructure nodes and a set of customers C including two customer types where customers in the subset C1 require a single connection (type-1) and customers in the subset C2 need to be redundantly connected (type-2). The aim is to seek a sub-graph of G with the smallest weight in which all customers are connected to infrastructure nodes and the connections are protected against failures. This is a NP-hard problem and it has been solved only with the objective of minimizing the network cost. In this paper, we introduce a new multi-objective approach to solve L-SNDP called ML-SNDP. These objectives are to minimize the network cost (total cost) and to minimize the maximal amount of sharing links between connections. Results of computational experiments reported show the efficiency of our proposal.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23

Similar content being viewed by others

References

  1. Kerivin H, Mahjoub AR (2005) Design of survivable networks: a survey. Networks 46(1):1–21

    Article  MathSciNet  MATH  Google Scholar 

  2. Wagner D, Raidl GR, Pferschy U, Mutzel P, Bachhiesl P (2007) A multi-commodity flow approach for the design of the last mile in real-world fiber optic networks. In: Waldmann KH, Stocker UM (eds) Operations research proceedings 2006, pp 197–202

  3. Leitner M, Raidl GR (2008) Lagrangian decomposition, meta-heuristics, and hybrid approaches for the design of the last mile in fiber optic networks. In: Blesa MJ et al. (eds) Hybrid meta-heuristics, vol 5296. Springer, Berlin, Heidelberg, pp 158–174

  4. Leitner M, Raidl GR (2010) Branch-and-cut and price for capacitated connected facility location. Technical report TR-186-1-10-01, Vienna University of Technology, Vienna, Austria

  5. Leitner M, Raidl GR (2010) Strong lower bounds for a survivable network design problem. In: International symposium on combinatorial optimization, Hammamet, Tunisia, pp 295–302

  6. IBM ILOG (2006) CPLEX optimizer performance benchmarks 10.0. http://www.ilog.com

  7. Wagner D, Pferschy U, Mutzel P, Raidl GR, Bachhiesl P (2007) A directed cut model for the design of the last mile in real-world fiber optic networks. In: Proceedings of the international network optimization conference 2007, Spa, Belgium, pp 1–6

  8. Bucsics T, Raidl G (2007) Metaheuristic approaches for designing survivable fiber-optic networks. In: Institute for computer graphics and algorithms of the Vienna University of Technology

  9. Ljubic I, Weiskircher R, Pferschy U, Klau G, Mutzel P, Fischetti M (2006) An algorithmic framework for the exact solution of the prize-collecting Steiner tree problem. Math Program Ser B 105(2–3):427–449

    Article  MathSciNet  MATH  Google Scholar 

  10. Da Cunha AS, Lucena A, Maculan N, Resende MGC (2009) A relax-and-cut algorithm for the prize-collecting Steiner problem in graph. Discrete Appl Math 157(6):1198–1217

    Article  MathSciNet  MATH  Google Scholar 

  11. Lucena A, Resende MGC (2004) Strong lower bounds for the prize collecting Steiner problem in graphs. Discrete Appl Math 141(1–3):277–294

    Article  MathSciNet  MATH  Google Scholar 

  12. Canuto SA, Resende MGC, Ribeiro CC (2001) Local search with perturbations for the prize-collecting Steiner tree problem in graphs. Networks 38:50–58

    Article  MathSciNet  MATH  Google Scholar 

  13. Chapovska O, Punnen AP (2006) Variations of the prize-collecting Steiner tree problem. Networks 47(4):199–205

    Article  MathSciNet  MATH  Google Scholar 

  14. Uchoa E (2006) Reduction tests for the prize-collecting Steiner problem. Op Res Lett 34(4):437–444

    Article  MathSciNet  MATH  Google Scholar 

  15. Fortz B, Labbe M (2006) Polyhedral approaches to the design of survivable networks. In: Resende MGC, Pardolas PM (eds) Handbook of optimization in telecommunications. Springer, Berlin, pp 367–389

    Chapter  Google Scholar 

  16. Stoer M (1992) Design of survivable networks. LNCS 1531. Springer, Heidelberg

    MATH  Google Scholar 

  17. Leitner M (2010) Solving two network design problems by mixed integer programming and hybrid optimization methods. Ph.D. thesis, Vienna University of Technology, Institute of Computer Graphics and Algorithms, Vienna, Austria

  18. Bachhiesl P (2005) The OPT- and the SST-problems for real world access network design basic definitions and test instances. Working report 01/2005, Carinthia Tech Institute, Department of Telematics and Network Engineering, Klagenfurt, Austria

  19. Vo TK, Nguyen MT, Huynh BTT (2012) Heuristic algorithms for solving survivability problem in the design of last mile communication network. In: Proceedings of the 4th Asian conference on intelligent information and database systems, Kaohsiung, Taiwan, pp 519–528

  20. Nguyen MT, Vo KT, Huynh BTT (2012) Heuristic Algorithms for Solving the Survivable Problem in the Design of Last Mile Communication Networks. In: Proceedings of the 9th IEEE—RIVF international conference on computing and communication technologies, Ho Chi Minh, Vietnam, pp 219–224

  21. Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6(2):182–197

    Article  Google Scholar 

  22. Huynh Thi Thanh Binh, Nguyen Thai Duong (2015) Heuristic and genetic algorithms for solving survivability problem in the design of last mile communication networks. Soft Computing 19:2619–2632

  23. Suurballe JW, Tarjan RE (1984) A quick method for finding shortest pairs of disjoint paths. Networks 14:325–336

    Article  MathSciNet  MATH  Google Scholar 

  24. Coello CAC, Sierra MR (2003) A multi-objective evolutionary algorithm based on coevolutionary concepts. In: The 2003 congress on evolutionary computation, vol 1, pp 482–489

  25. Binh HTT, Bui LT, Ha NST, Ishibuchi H (2014) A multi-objective approach for solving the survivable network design problem with simultaneous unicast and anycast flows. Appl Soft Comput 24:1145–1154

    Article  Google Scholar 

  26. Farnsworth M, Benkhelifa E, Tiwari A, Zhu M, Moniri M (2011) An efficient evolutionary multi-objective framework for MEMS design optimisation: validation, comparison and analysis. Memet Comput 3(3):175–197

    Article  Google Scholar 

  27. Joshi R, Deshpande B (2014) Empirical and analytical study of many-objective optimization problems: analysing distribution of nondominated solutions and population size for scalability of randomized heuristics. Memet Comput 6(2):133–145

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported by the project “Advanced methods in Evolutionary Computation in approximating solutions for combinatorial optimization problems”, Grant no. 102.01-2015.12, funded by National Foundation for Science and Technology Development, Vietnam.

Author information

Authors and Affiliations

  1. Le Quy Don Technical University, Hanoi, Vietnam

    Lam Thu Bui

  2. Hanoi University of Science and Technology, Hanoi, Vietnam

    Huynh Thi Thanh Binh

Authors
  1. Lam Thu Bui
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Huynh Thi Thanh Binh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Huynh Thi Thanh Binh.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bui, L.T., Thi Thanh Binh, H. A survivable design of last mile communication networks using multi-objective genetic algorithms. Memetic Comp. 8, 97–108 (2016). https://doi.org/10.1007/s12293-015-0177-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12293-015-0177-7

Keywords

Search

Navigation