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Reseach Article

Hybrid Multi-Objectives Genetic Algorithms and Immigrants Scheme for Dynamic Routing Problems in Mobile Networks

by Khalil Ibrahim Mohammad Abuzanouneh
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 164 - Number 5
Year of Publication: 2017
Authors: Khalil Ibrahim Mohammad Abuzanouneh

Khalil Ibrahim Mohammad Abuzanouneh . Hybrid Multi-Objectives Genetic Algorithms and Immigrants Scheme for Dynamic Routing Problems in Mobile Networks. International Journal of Computer Applications. 164, 5 ( Apr 2017), 49-57. DOI=10.5120/ijca2017913641

@article{ 10.5120/ijca2017913641,
author = { Khalil Ibrahim Mohammad Abuzanouneh },
title = { Hybrid Multi-Objectives Genetic Algorithms and Immigrants Scheme for Dynamic Routing Problems in Mobile Networks },
journal = { International Journal of Computer Applications },
issue_date = { Apr 2017 },
volume = { 164 },
number = { 5 },
month = { Apr },
year = { 2017 },
issn = { 0975-8887 },
pages = { 49-57 },
numpages = {9},
url = { },
doi = { 10.5120/ijca2017913641 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-07T00:10:30.771905+05:30
%A Khalil Ibrahim Mohammad Abuzanouneh
%T Hybrid Multi-Objectives Genetic Algorithms and Immigrants Scheme for Dynamic Routing Problems in Mobile Networks
%J International Journal of Computer Applications
%@ 0975-8887
%V 164
%N 5
%P 49-57
%D 2017
%I Foundation of Computer Science (FCS), NY, USA

In this paper, the author discusses the main concept of intelligent optimization techniques, artificial neural networks, and new genetic algorithms to solve the multi-objective multicast routing problems with shortest path (SP) problem that used in the addresses networks and improve all processes addressing in the wireless communications based on multi-objective optimization. The most important characteristics in mobile wireless networks is the topology dynamics and the network topology changes over time, the routing problem (SPRP) in mobile ad hoc networks (MANETs) turns out to be a dynamic optimization problem[13], the hybrid immigrants multiple-objective genetic algorithm (HIMOGAs) in the real-world are dynamic in nature, that has objective functions, constraints, and parameters, the dynamic optimization problems (DOPs) are a big challenges to evolutionary multi-objective, since any environmental change may affect the objective vector, constraints, and parameters, HIMOGA for the optimization goal is to track the moving of parameters and get a sequence of approximations solutions over time. The quantity of services (QoS) is supporting guarantee for all data traffic and getting the maximizing utilization for network, the QoS based on multicast routing offer significant challenges, and increases to use an efficient multicast routing protocol that will be able to check multicast routing and satisfying QoS constraints, The author propose to use HIMOGAs and SP algorithm to solve multicast problem that produces new generation wireless networks with immigrants schema to get high-quality solutions after each change and satisfying all objectives.

  1. F. Oppacher and M. Weinberg, “The shifting balance genetic algorithm: Improving the GA in a dynamic environment,” Proc. 1999 Genet. Evol. Comput. Conf., vol. 1, pp. 504–510.
  2. C. W. Ahn, R. S. Ramakrishna, C. G. Kang, and I. C. Choi, “Shortest path routing algorithm using Hopfield neural network,” Electron. Lett, vol. 37, no. 19, pp. 1176–1178, Sep. 2001.
  3. C. W. Ahn and R. S. Ramakrishna, “A genetic algorithm for shortest path routing problem and the sizing of populations,” IEEE Trans. Evol. Comput., vol. 6, no. 6, pp. 566–579, Dec. 2002.
  4. J.Branke, “Memory enhanced evolutionary algorithms for changing optimization problems,” inProc.1999Congr. Evol. Comput, pp. 1875–1882.
  5. J. Branke, Evolutionary Optimization in Dynamic Environments. Norwell, MA: Kluwer, 2002.
  6. Parsa, M., Zhu, Q., Garcia-Luna-Aceves, J. (1998). An Iterative Algorithm for Delay-constrained Minimum-cost Multicasting. IEEE/ACM Trans. Netw., 6, 461–474.
  7. Jain, K., Padhye, J., Padmanabhan, V., and Qiu, L. (2003). Impact of Interference on Multi-hop Wireless Network Performance. Proc. MobiCom 2003, 66–80.
  8. B. L. Sun, S.C.Pi, C. GUI, et al., Multiple constraints QoS multicast routing optimization algorithm in MANET based on GA, Progress in Natural Science 18 (3) (2008)331–336.
  9. J. Branke, T. Kaußler, C. Schmidt, and H. Schmeck, “A multi population approach to dynamic optimization problems,”inProc. 4thInt.Conf.Adaptive Comput. Des. Manuf., 2000, pp. 299–308.
  10. X. Yu, K. Tang, and X. Yao, “An immigrants scheme based on environmental information for genetic algorithms in changing environments,” in Proc. 2008 Congr. Evol. Comput., pp. 1141–1147.
  11. M. Parsa, Q. Zhu, and J. Garcia-Luna Aceves, “An iterative algorithm for delay-constrained minimum-cost multicasting,” IEEE/ACMTrans. Netw., vol. 6, no. 4, pp. 461–474, Aug. 1998.
  12. R. Tinos and S. Yang, “A self-organizing random immigrants genetic algorithm for dynamic optimization problems,” Genet. Program. Evol. Mach., vol. 8, no. 3, pp. 255–286, Sep. 2007.
  13. C. -K. Toh, Ad Hoc Mobile Wireless Networks: Protocols and Systems. Englewood Cliffs, NJ: Prentice-Hall, 2002.
  14. S. Yang, “Population-based incremental learning with memory scheme for changing environments,” in Proc. 2005 Genet. Evol. Comput. Conf., vol. 1, pp. 711–718.
  15. S. Yang, “Genetic algorithms with elitism-based immigrants for changing optimization problems,” in Proc. EvoWorkshops 2007: Appl. Evol. Comput. (Lecture Notes in Computer Science), vol. 4448, pp. 627–636.
  16. S. Yang,“Genetic algorithms with memory-and elitism-based immigrants in dynamic environments,” Evol. Comput., vol. 16, no. 3, pp. 385–416, Sep. 2008.
  17. S. Yang and R. Tinos, “A hybrid immigrants scheme for genetic algorithms in dynamic environments,” Int. J. Autom. Comput., vol. 4, no. 3, pp. 243– 254, Jul. 2007.
  18. Baolin Sun and Layuan Li, Multiple Constraints Based QoS Multicast Routing: Model and Algorithms, Journal of Systems Engineering and Electronics, Vol. 15, No. 4, 2004.
  19. V. Rodoplu and T. H. Meng, “Minimum energy mobile wireless networks,” IEEE. J. Sel. Areas Commun., vol. 17, no. 8, pp. 1205–1220, 2008.
  20. H. Liu, B. Zhang, J. Zheng, and H. T. Mouftah, “An energy-efficient localized topology control algorithm for wireless ad hoc and sensor networks,” Int. J. Commun. Syst., vol. 21, no. 11, pp. 1205–1220, 2008.
  21. Wang, X., Cao, J., Cheng, H., and Huang, M. (2006). QoS Multicast Routing for Multimedia Group Communications Using Intelligent Computational Methods. Comput. Comm., 29, 2217–2229.
  22. S. -Y. Tseng, Y. -M. Huang, C. -C. Lin, Genetic algorithm for delay and degree-constrained multimedia broadcasting on overlay networks, Computer Communications 29 (17) (2006)3625–3632.
  23. Das and C. Martel,“Stochastic shortest path with unlimited hops,” Inf. Process. Lett. vol. 109, no. 5, pp. 290–295, 2009.
  24. Yen, Yun-Sheng, et al. "Flooding-limited and multi-constrained QoS multicast routing based on the genetic algorithm for MANETs." Mathematical and Computer Modelling 53.11 (2011): 2238-2250.
Index Terms

Computer Science
Information Sciences


Hybrid immigrants multiple-objective dynamic shortest path routing problem Dynamic immigrants scheme.