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Multiobjective Genetic Algorithm for Minimum Weight Minimum Connected Dominating Set

  • Dinesh Rengaswamy
  • Subham Datta
  • Subramanian Ramalingam
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 736)

Abstract

Connected Dominating Set (CDS) is a connected subgraph of a graph G with the property that any given node in G either belongs to the CDS or is adjacent to one of the CDS nodes. Minimum Connected Dominating Sets (MCDS), where the CDS nodes are sought to be minimized, are of special interests in various fields like Computer networks, Biological networks, Social networks, etc., since they represent a set of minimal important nodes. Similarly, Minimum Weight Connected Dominating Sets (MWCDS), where the connected weights among the CDS nodes are sought to be minimized, is also of interest in many research application. This work is based on the hypothesis that a CDS with both the properties of minimum size and minimum weight optimized would enhance performance in many applications where CDS is used. Though there are a good number of approximate and heuristic algorithms for MCDS and MWMCDS, there is no work to the best of our knowledge, that optimizes the generated CDS with respect to both the size and weight. A Multiobjective Genetic Algorithm for Minimum Weight Minimum Connected Dominating Set (MOGA-MWMCDS) is proposed. Performance analysis based on a Wireless Sensor Network (WSN) scenario indicates the efficiency of the proposed MOGA-MWMCDS and supports the advantage of MWMCDS use.

Keywords

Multiobjective Genetic Algorithm Minimum Connected Dominating Set Minimum Weight Connected Dominating Set Minimum Weight Minimum Connected Dominating Set Evolutionary algorithm Wireless sensor networks 

References

  1. 1.
    Das, B., Sivakumar, R., Bharghavan, V.: Routing in ad hoc networks using a spine. In: Proceedings of the International Conference on Computer Communications and Networks, ICCCN, pp. 34–39 (1997)Google Scholar
  2. 2.
    Mohanty, J.P., Mandal, C., Reade, C.: Distributed construction of minimum Connected Dominating Set in wireless sensor network using two-hop information. Comput. Netw. 123, 137–152 (2017)CrossRefGoogle Scholar
  3. 3.
    Wang, Y., Wang, W., Li, X.-Y.: Distributed low-cost backbone formation for wireless ad hoc networks. In: Proceedings of the International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc), pp. 2–13 (2005)Google Scholar
  4. 4.
    Torkestani, J.A.: Backbone formation in wireless sensor networks. Sens. Actuators A: Phys. 185, 117–126 (2012)CrossRefGoogle Scholar
  5. 5.
    Lima, M.P., Alexandre, R.F., Takahashi, R.H.C., Carrano, E.G.: A comparative study of multiobjective evolutionary algorithms for wireless local area network design. In: Proceedings of the IEEE Congress on Evolutionary Computation, CEC 2017, pp. 968–975 (2017). Article no. 7969413Google Scholar
  6. 6.
    Massobrio, R., Bertinat, S., Nesmachnow, S., Toutouh, J., Alba, E.: Smart placement of RSU for vehicular networks using multiobjective evolutionary algorithms. In: Latin-America Congress on Computational Intelligence, LA-CCI 2015 (2016). Article no. 7435974Google Scholar
  7. 7.
    Gu, F., Liu, H.-L., Cheung, Y.-M., Xie, S.: Optimal WCDMA network planning by multiobjective evolutionary algorithm with problem-specific genetic operation. Knowl. Inf. Syst. 45(3), 679–703 (2015)CrossRefGoogle Scholar
  8. 8.
    He, J., Cai, Z., Ji, S., Beyah, R., Pan, Y.: A genetic algorithm for constructing a reliable MCDS in probabilistic wireless networks. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). LNCS, vol. 6843, pp. 96–107 (2011)Google Scholar
  9. 9.
    Manohari, D., Anandha Mala, G.S.: An evolutionary algorithmic approach to construct connected dominating set in MANETs. IET Semin. Digest (4) (2012)Google Scholar
  10. 10.
    He, J., Ji, S., Yan, M., Pan, Y., Li, Y.: Load-balanced CDS construction in wireless sensor networks via genetic algorithm. Int. J. Sens. Netw. 11(3), 166–178 (2012)CrossRefGoogle Scholar
  11. 11.
    Kumar, G., Rai, M.K.: An energy efficient and optimized load balanced localization method using CDS with one-hop neighbourhood and genetic algorithm in WSNs. J. Netw. Comput. Appl. 78, 73–82 (2017)CrossRefGoogle Scholar
  12. 12.
    Hosseini, E.S., Esmaeelzadeh, V., Eslami, M.: A hierarchical sub-chromosome genetic algorithm (HSC-GA) to optimize power consumption and data communications reliability in wireless sensor networks. Wirel. Pers. Commun. 80(4), 1579–1605 (2015)CrossRefGoogle Scholar
  13. 13.
    Dagdeviren, Z.A., Aydin, D., Cinsdikici, M.: Two population-based optimization algorithms for minimum weight connected dominating set problem. Appl. Soft Comput. J. 59, 644–658 (2017)CrossRefGoogle Scholar
  14. 14.
    Pizzuti, C.: A multiobjective genetic algorithm to find communities in complex networks. IEEE Trans. Evol. Comput. 16(3), 418–430 (2012). Article no. 6045331CrossRefGoogle Scholar
  15. 15.
    Nazarieh, M., Wiese, A., Will, T., Hamed, M., Helms, V.: Identification of key player genes in gene regulatory networks. BMC Syst. Biol. 10(1), Article no. 88 (2016)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computer SciencePondicherry UniversityPuducherryIndia

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