Skip to main content
SpringerLink
Log in

Genetic Algorithms for Dynamic Routing Problems in Mobile Ad Hoc Networks

  • Conference paper
Evolutionary Computation for Dynamic Optimization Problems

Part of the book series: Studies in Computational Intelligence ((SCI,volume 490))

Abstract

Routing plays an important role in various types of networks. There are two main ways to route the packets, i.e., unicast and multicast. In most cases, the unicast routing problem is to find the shortest path between two nodes in the network and the multicast routing problem is to find an optimal tree spanning the source and all the destinations. In recent years, both the shortest path routing and the multicast routing have been well addressed using intelligent optimization techniques. With the advancement in wireless communications, more and more mobile wireless networks appear, e.g., mobile ad hoc networks (MANETs). One of the most important characteristics in MANETs is the topology dynamics, that is, the network topology changes over time due to energy conservation or node mobility. Therefore, both routing problems turn out to be dynamic optimization problems inMANETs. In this chapter, we investigate a series of dynamic genetic algorithms to solve both the dynamic shortest path routing problem and the dynamic multicast routing problem in MANETs. The experimental results show that these specifically designed dynamic genetic algorithms can quickly adapt to environmental changes (i.e., the network topology changes) and produce high quality solutions after each change.

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 169.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover 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. Adelstein, F., Richard, G., Schwiebert, L.: Distributed multicast tree generation with dynamic group membership. Comput. Commun. 26(10), 1105–1128 (2003)

    Article  Google Scholar 

  2. Aharoni, E., Cohen, R.: Restricted dynamic Steiner trees for scalable multicast in datagram networks. IEEE/ACM Trans. Netw. 6(3), 286–297 (1998)

    Article  Google Scholar 

  3. Ahn, C.W., Ramakrishna, R.S., Kang, C.G., Choi, I.C.: Shortest path routing algorithm using hopfield neural network. Electron. Lett. 37(19), 1176–1178 (2001)

    Article  Google Scholar 

  4. Ahn, C.W., Ramakrishna, R.S.: A genetic algorithm for shortest path routing problem and the sizing of populations. IEEE Trans. Evol. Comput. 6(6), 566–579 (2002)

    Article  Google Scholar 

  5. Branke, J.: Memory enhanced evolutionary algorithms for changing optimization problems. In: Proc. 1999 Congr. Evol. Comput., pp. 1875–1882 (1999)

    Google Scholar 

  6. Branke, J., Kaußler, T., Schmidt, C., Schmeck, H.: A multi-population approach to dynamic optimization problems. In: Proc. 4th Int. Conf. Adaptive Comput. Des. Manuf., pp. 299–308 (2000)

    Google Scholar 

  7. Cheng, H., Wang, X., Yang, S., Huang, M.: A multipopulation parallel genetic simulated annealing based QoS routing and wavelength assignment integration algorithm for multicast in optical networks. Appl. Soft Comput. 9(2), 677–684 (2009)

    Article  Google Scholar 

  8. Cheng, H., Yang, S.: Genetic algorithms with immigrants schemes for dynamic multicast problems in mobile ad hoc networks. Eng. Appl. Artif. Intel. 23(5), 806–819 (2010)

    Article  Google Scholar 

  9. Cobb, H.G., Grefenstette, J.J.: Genetic algorithms for tracking changing environments. In: Proc. 5th Int. Conf. Genetic Algorithms, pp. 523–530 (1993)

    Google Scholar 

  10. Cordeiro, C., Gossain, H., Agrawal, D.: Multicast over wireless mobile ad hoc networks: present and future directions. IEEE Netw. 17(1), 52–59 (2003)

    Article  Google Scholar 

  11. Dasgupta, D., McGregor, D.: Nonstationary function optimization using the structured genetic algorithm. In: Proc. 2nd Int. Conf. Parallel Problem Solving from Nature, pp. 145–154 (1992)

    Google Scholar 

  12. Din, D.: Anycast routing and wavelength assignment problem on WDM network. IEICE Trans. Commun. E88-B(10), 3941–3951 (2005)

    Article  Google Scholar 

  13. Grefenstette, J.J.: Genetic algorithms for changing environments. In: Proc. 2nd Int. Conf. Parallel Problem Solving from Nature, pp. 137–144 (1992)

    Google Scholar 

  14. Helvig, C., Robins, G., Zelikovsky, A.: An improved approximation scheme for the group Steiner problem. Networks 37(1), 8–20 (2000)

    Article  MathSciNet  Google Scholar 

  15. Holland, J.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor (1975)

    Google Scholar 

  16. Hwang, F., Richards, D.: Steiner tree problems. Networks 22(1), 55–89 (1992)

    Article  MathSciNet  MATH  Google Scholar 

  17. Jia, X., Pissinou, N., Makki, K.: A real-time multicast routing algorithm for multimedia applications. Comput. Commun. 20(12), 1098–1106 (1997)

    Article  Google Scholar 

  18. Jia, X.: A distributed algorithm of delay-bounded multicast routing for multimedia applications in wide area networks. IEEE/ACM Trans. Netw. 6(6), 828–837 (1998)

    Article  Google Scholar 

  19. Khuller, S., Raghavachari, B., Young, N.: Balancing minimum spanning and shortest path trees. Algorithmica 14(4), 305–321 (1995)

    Article  MathSciNet  MATH  Google Scholar 

  20. Lee, S., Soak, S., Kim, K., Park, H., Jeon, M.: Statistical properties analysis of real world tournament selection in genetic algorithms. Appl. Intel. 28(2), 195–205 (2008)

    Article  Google Scholar 

  21. Lewis, J., Hart, E., Ritchie, G.: A comparison of dominance mechanisms and simple mutation on non-stationary problems. In: Eiben, A.E., Bäck, T., Schoenauer, M., Schwefel, H.-P. (eds.) PPSN 1998. LNCS, vol. 1498, pp. 139–148. Springer, Heidelberg (1998)

    Chapter  Google Scholar 

  22. Louis, S., Xu, Z.: Genetic algorithms for open shop scheduling and re-scheduling. In: Proc. 11th ISCA Int. Conf. Comput. Their Appl., pp. 99–102 (1996)

    Google Scholar 

  23. Mohemmed, A.W., Sahoo, N.C., Geok, T.K.: Solving shortest path problem using particle swarm optimization. Appl. Soft Comput. 8(4), 1643–1653 (2008)

    Article  Google Scholar 

  24. Mori, H., Nishikawa, Y.: Adaptation to changing environments by means of the memory based thermodynamical genetic algorithm. In: Proc. 7th Int. Conf. Genetic Algorithms, pp. 299–306 (1997)

    Google Scholar 

  25. Morrison, R.W., De Jong, K.A.: Triggered hypermutation revisited. In: Proc. 2000 Congr. Evol. Comput., vol. 2, pp. 1025–1032 (2000)

    Google Scholar 

  26. Siva Ram Murthy, C., Manoj, B.S.: Ad Hoc Wireless Networks: Architectures and Protocols. Prentice Hall PTR (2004)

    Google Scholar 

  27. Narvaez, R., Siu, K.-Y., Tzeng, H.-Y.: New dynamic algorithms for shortest path tree computation. IEEE/ACM Trans. Netw. 8(6), 734–746 (2000)

    Article  Google Scholar 

  28. Oh, S., Ahn, C., Ramakrishna, R.: A genetic-inspired multicast routing optimization algorithm with bandwidth and end-to-end delay constraints. In: King, I., Wang, J., Chan, L.-W., Wang, D. (eds.) ICONIP 2006. LNCS, vol. 4234, pp. 807–816. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  29. Oliveira, C., Pardalos, P.: A survey of combinatorial optimization problems in multicast routing. Comput. & Oper. Res. 32(8), 1953–1981 (2005)

    Article  MATH  Google Scholar 

  30. Oppacher, F., Wineberg, M.: The shifting balance genetic algorithm: improving the GA in a dynamic environment. In: Proc. 1999 Genetic Evol. Comput. Conf., vol. 1, pp. 504–510 (1999)

    Google Scholar 

  31. Papadimitriou, C., Steiglitz, K.: Combinatorial Optimization: Algorithms and Complexity. Dover Publications Inc., NY (1998)

    MATH  Google Scholar 

  32. Parsa, M., Zhu, Q., Garcia-Luna-Aceves, J.: An iterative algorithm for delay-constrained minimum-cost multicasting. IEEE/ACM Trans. Netw. 6(4), 461–474 (1998)

    Article  Google Scholar 

  33. Robins, G., Zelikovsky, A.: Improved Steiner tree approximation in graphs. In: Proc. ACM/SIAM Symp. on Discrete Algorithms, pp. 770–779 (2000)

    Google Scholar 

  34. Tinos, R., Yang, S.: A self-organizing random immigrants genetic algorithm for dynamic optimization problems. Genet. Program. Evolvable Mach. 8(3), 255–286 (2007)

    Article  Google Scholar 

  35. Trojanowski, K., Michalewicz, Z.: Evolutionary optimization in non-stationary environments. J. Comput. Sci. Tech. 1(2), 93–124 (2000)

    Google Scholar 

  36. Uyar, A., Harmanci, A.: A new population based adaptive dominance change mechanism for diploid genetic algorithms in dynamic environments. Soft Comput. 9(11), 803–815 (2005)

    Article  MATH  Google Scholar 

  37. Vavak, F., Fogarty, T.C.: A comparative study of steady state and generational genetic algorithms for use in nonstationary environments. In: Fogarty, T.C. (ed.) AISB-WS 1996. LNCS, vol. 1143, pp. 297–304. Springer, Heidelberg (1996)

    Chapter  Google Scholar 

  38. Wang, X., Cao, J., Cheng, H., Huang, M.: QoS multicast routing for multimedia group communications using intelligent computational methods. Comput. Commun. 29(12), 2217–2229 (2006)

    Article  Google Scholar 

  39. Weicker, K.: Evolutionary Algorithms and Dynamic Optimization Problems. Der andere Verlag, Osnabrück (2003)

    Google Scholar 

  40. Xu, Y., Salcedo-Sanz, S., Yao, X.: Metaheuristic approaches to traffic grooming in WDM optical networks. Int. J. of Comput. Intel. Appl. 5(2), 231–249 (2005)

    Article  Google Scholar 

  41. Yang, S.: Memory-based immigrants for genetic algorithms in dynamic environments. In: Proc. 2005 Genetic Evol. Comput. Conf., vol. 2, pp. 1115–1122 (2005)

    Google Scholar 

  42. Yang, S.: Genetic algorithms with elitism-based immigrants for changing optimization problems. In: Giacobini, M. (ed.) EvoWorkshops 2007. LNCS, vol. 4448, pp. 627–636. Springer, Heidelberg (2007)

    Google Scholar 

  43. Yang, S.: Genetic algorithms with memory- and elitism-based immigrants in dynamic environments. Evol. Comput. 16(3), 385–416 (2008)

    Article  Google Scholar 

  44. Yang, S., Cheng, H., Wang, F.: Genetic algorithms with immigrants and memory schemes for dynamic shortest path routing problems in mobile ad hoc networks. IEEE Trans. Syst., Man, Cybern. C, Appl. Rev. 40(1), 52–63 (2010)

    Article  Google Scholar 

  45. Yang, S., Ong, Y.-S., Jin, Y. (eds.): Evolutionary Computation in Dynamic and Uncertain Environments. Springer (2007)

    Google Scholar 

  46. Yang, S., Tinos, R.: A hybrid immigrants scheme for genetic algorithms in dynamic environments. Int. J. Autom. Comput. 4(3), 243–254 (2007)

    Article  Google Scholar 

  47. Yang, S., Tinos, R.: Hyper-selection in dynamic environments. In: Proc. 2008 Congr. Evol. Comput., pp. 3185–3192 (2008)

    Google Scholar 

  48. Yang, S., Yao, X.: Experimental study on population-based incremental learning algorithms for dynamic optimization problems. Soft Comput. 9(11), 815–834 (2005)

    Article  MATH  Google Scholar 

  49. Yang, S., Yao, X.: Population-based incremental learning with associative memory for dynamic environments. IEEE Trans. Evol. Comput. 12(5), 542–561 (2008)

    Article  Google Scholar 

  50. Yong, K., Poo, G., Cheng, T.: Proactive rearrangement in delay constrained dynamic membership multicast. Comput. Commun. 31(10), 2566–2580 (2008)

    Article  Google Scholar 

  51. Yu, X., Tang, K., Chen, T., Yao, X.: Empirical analysis of evolutionary algorithms with immigrants schemes for dynamic optimization. Memetic Comput. 1(1), 3–24 (2009)

    Article  Google Scholar 

  52. Yu, X., Tang, K., Yao, X.: An immigrants scheme based on environmental information for genetic algorithms in changing environments. In: Proc. 2008 Congr. Evol. Comput., pp. 1141–1147 (2008)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Technology, University of Bedfordshire, Park Square, Luton, LU1 3JU, U.K.

    Hui Cheng

  2. Centre for Computational Intelligence (CCI), School of Computer Science and Informatics, De Montfort University, The Gateway, Leicester, LE1 9BH, U.K.

    Shengxiang Yang

Authors
  1. Hui Cheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shengxiang Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hui Cheng .

Editor information

Editors and Affiliations

  1. , School of Computer Science and Informati, De Montfort University, The Gateway, Leicester, LE1 9BH, United Kingdom

    Shengxiang Yang

  2. , School of Computer Science, University of Birmingham, Birmingham, B15 2TT, United Kingdom

    Xin Yao

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Cheng, H., Yang, S. (2013). Genetic Algorithms for Dynamic Routing Problems in Mobile Ad Hoc Networks. In: Yang, S., Yao, X. (eds) Evolutionary Computation for Dynamic Optimization Problems. Studies in Computational Intelligence, vol 490. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38416-5_14

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-38416-5_14

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-38415-8

  • Online ISBN: 978-3-642-38416-5

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation