[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content
Springer Nature Link
Log in

Parallel Big Bang–Big Crunch Global Optimization Algorithm: Performance and its Applications to routing in WMNs

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

This paper presents the performance of Parallel Big Bang–Big Crunch (PB3C) global optimization algorithm on CEC-2014 test suite. The performance is compared with 16 other algorithms. It has been observed that PB3C gave best performance on 7 functions of the test bench. Out of seven, for 6 functions it gave the unmatched best performance whereas on one count its performance was equaled by other algorithm as well. Further this paper proposes a PB3C based new routing approach to wireless mesh networks (WMNs). Being dynamic; routing is a challenging issue in WMNs. The approach is a near shortest path route evaluation approach. The approach was simulated on MATLAB. The performance was compared with 7 other approaches namely ad hoc on-demand distance vector, dynamic source routing, ant colony optimization, biogeography based optimization, firefly algorithm, BAT and simple Big Bang–Big Crunch based approaches. For WMNs of size 1000 nodes and above the PB3C was observed to outperform rest of the 7 algorithms.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. Erol, K., & Eksin, I. (2006). A new optimization method: Big Bang–Big crunch. Advances in Engineering Software, 37, 106–111.

    Article  Google Scholar 

  2. Kumbasar, T., Yesil, E., Eksin, I., & Guzelkaya, M. (2008). Inverse fuzzy model control with online adaptation via Big Bang–Big crunch optimization. In ISCCSP 2008, Malta (pp. 697–703) (March 12–14, 2008).

  3. Kripka, M., & Kripka, R.M.L. (2008). Big crunch optimization method. In International conference on engineering optimization, Brazil.

  4. Kumar, S., Walia, S. S., & Singh, A. (2013). Parallel Big Bang–Big Crunch algorithm. International Journal of Advanced Computing, Recent Science Publications, 46(3), 1330–1335.

    Google Scholar 

  5. Waharte, S., Boutaba, R., Iraqi, Y., & Ishibashi, B. (2006). Routing protocols in wireless mesh networks: Challenges and design considerations. Multimedia Tools Applications, 29(3), 285–303.

    Article  Google Scholar 

  6. Kumar, S., Singh, B., & Sharma, S. (2013). Soft computing framework for routing in wireless mesh networks: An integrated cost function approach. International Journal of Electronics Computer and Communications Technologies (IJECCT), 3(3), 25–32.

    Google Scholar 

  7. Sharma, S., Kumar, S., & Singh, B. (2014). Hybrid intelligent routing in wireless mesh networks: Soft computing based approaches. International Journal of Intelligent Systems and Applications, Modern Education and Science Press, 1, 45–57.

    Article  Google Scholar 

  8. Kumar, S., Singh, B., & Sharma, S. (2015). Routing in wireless mesh networks: Three new nature inspired approaches. Wireless Personal Communications: An International Journal, 83(4), 3157–3179.

    Article  Google Scholar 

  9. Yang, S., Cheng, H., & Wang, F. (2010). Genetic algorithms with immigrants and memory schemes for dynamic shortest path routing problems in mobile ad hoc networks. IEEE Transactions on Systems, MAN, and Cybernetics-Part C: Applications and Reviews, 40(1), 52–63.

    Article  Google Scholar 

  10. Adya, A., Bahl, P., Padhye, J., Wolman, A., & Zhou, L. (2004). A multi radio unification protocol for IEEE 802.11 wireless networks. In International conference on broadcast networks (Broad Nets), San Jose, California, USA, (pp. 344–354) (October 25 29, 2004).

  11. Yang, Y., Wang, J. & Kravets, R. (2005). Interference-aware load balancing for multihop wireless networks. In Techical Report UIUCDCS-R-2005-2526, Department of Computer Science, University of Illinois at Urbana-Champaign (pp. 1–16).

  12. De Couto, D.S.J., Aguayo, D., Bicket, J., & Morris, R. (2003). A high-throughput path metric for multihop wireless routing. In Proceedings of ACM annual international conference on mobile computing and networking (MOBICOM), San Diego, CA, USA (pp. 134–146) (September 14–19, 2003).

  13. Draves, R., Padhye, J., & Zill, B. (2004). Comparisons of routing metrics for static multi-hop wireless networks. In ACM annual conference of the special interest group on data communication (SIGCOMM), Portland, Oregon, USA (pp. 133–144) (August 30–September 03 , August 2004).

  14. Jakllari, G., Eidenbenz, S., Hengartner, N., Krishnamurthy, S., & Faloutsos, M. (2008). Link positions matter: A non commutative routing metric for wireless mesh networks. In Proceedings of IEEE annual conference on computer communications (INFOCOM),n Phoenix, Arizona, USA (pp. 744–752) (April 13–18, 2008).

  15. Koksal, C. E., & Balakrishnan, H. (2006). Quality-aware routing metrics for time-varying wireless mesh networks. IEEE Journal on Selected Areas in Communications, 24(11), 1984–1994.

    Article  Google Scholar 

  16. [Draves, R., Padhye, J., & Zill, B. (2004). Routing in multi-radio. Multihop wireless Mesh networks. In ACM annual international conference on mobile computing and networking (Mobi Con04), Philadelphia, Pennsylvania, USA (pp. 114–128).

  17. Liu, T., & Liao, W. (2006). Capacity-aware routing in multi-channel multi-rate wireless mesh networks. In Proceedings of IEEE international conference on communications (ICC) (pp. 1971–1976).

  18. Ma, L., Zhang, Q., Xiong, Y., & Zhu, W. (2005). Interference aware metric for dense multi-hop wireless network. In Proceedings of IEEE international conference on communications (ICC) (pp. 1261–1265).

  19. Karbaschi, G., & Fladenmuller, A. (2005). A link quality and congestion-aware cross layer metric for multi-hop wireless routing. In Proceedings of IEEE MASS05 (pp. 7–11).

  20. Kumar, S., Singh, B., & Sharma, S. (2015). Routing in wireless mesh networks: Three new nature inspired approaches. Wireless Personal Communications, 85(4), 3157–3179.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Computational Intelligence Laboratory, Baddi University of Emerging Sciences and Technology, Baddi, HP, India

    Shakti Kumar & Amar Singh

  2. I.K. Gujral Punjab Technical University, Kapurthala, Jalandhar, Punjab, India

    Sukhbir Walia

Authors
  1. Shakti Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Amar Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sukhbir Walia
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shakti Kumar.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kumar, S., Singh, A. & Walia, S. Parallel Big Bang–Big Crunch Global Optimization Algorithm: Performance and its Applications to routing in WMNs. Wireless Pers Commun 100, 1601–1618 (2018). https://doi.org/10.1007/s11277-018-5656-y

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-018-5656-y

Keywords

Search

Navigation