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Variable Neighborhood Search Algorithms for the Node Placement Problem in Multihop Networks

  • Kengo KatayamaEmail author
  • Yusuke Okamoto
  • Elis Kulla
  • Noritaka Nishihara
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 2)

Abstract

We consider a problem of finding an optimal node placement that minimizes the amount of traffic by reducing the weighted hop distances in multihop networks. The problem is called Node Placement Problem (NPP) and is known to be NP-hard. Therefore, several heuristic and metaheuristic algorithms have been proposed for solving NPP, such as local search, genetic algorithm, simulated annealing, tabu search, iterated local search, ant colony optimization, etc. Although Variable Neighborhood Search (VNS) is known to be one of the most promising and efficient metaheuristic algorithms for optimization problems, VNS has not been shown for NPP yet. In this paper we propose VNS algorithms for NPP. The proposed VNSs consist of two phases: local search phase to obtain a local optimum and perturbation phase to get out of the corresponding valley in the search space. We show six types of neighborhood change schemes for the perturbation phase of VNS, and through computational experiments, we compare each performance of six VNSs incorporating k-swap local search, called VNS1, VNS2,…, VNS6. The experimental results indicate that VNS4 outperformed the others for large problem instances particularly, which adopts a suitable perturbation size selected by exploring from the upper bound that is adaptively lower in the search.

Keywords

Local Search Metaheuristic Algorithm Iterate Local Search Node Placement Local Search Phase 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Garey, M.R., Johnson, D.S.: Computers and Intractability: A Guide to the Theory of NPCompleteness. Freeman, New York (1979)Google Scholar
  2. 2.
    Gendreau, M., Potvin, J.Y.: Handbook of Metaheuristics. International Series in Operations Research & Management Science. Springer (2010)Google Scholar
  3. 3.
    Hansen, P., Mladenovi´c, N., Moreno-P´erez, J.A.: Variable neighbourhood search: Methods and applications. Annals of Operations Research 175(1), 367–407 (2010)Google Scholar
  4. 4.
    Katayama, K., Akagi, Y., Kulla, E., Minamihara, H., Nishihara, N.: New kick operators in iterated local search based metaheuristic for solving the node placement problem in multihop networks. In: Proceedings of the 17th International Conference on Network-Based Information Systems (NBiS-2014), pp. 141–148 (2014)Google Scholar
  5. 5.
    Katayama, K., Yamashita, H., Narihisa, H.: Variable depth search and iterated local search for the node placement problem in multihop WDM lightwave networks. In: Proceedings of IEEE Congress on Evolutionary Computation, pp. 3508–3515 (2007)Google Scholar
  6. 6.
    Kato, M., Oie, Y.: Reconfiguration algorithms based on meta-heuristics for multihop WDM lightwave networks. In: Proc. IEEE International Conference on Communications, pp. 1638–1644 (2000)Google Scholar
  7. 7.
    Kernighan, B., Lin, S.: An efficient heuristic procedure for partitioning graphs. Bell System Technical Journal 49, 291–307 (1970)Google Scholar
  8. 8.
    Kitani, T., Yonedu, M., Funabiki, N., Nakanishi, T., Okayama, K., Higashino, T.: A two-stage hierarchical algorithm for wavelength assignment in WDM-based bidirectional manhattan street networks. In: Proc. the 11th IEEE International Conf. on Networks, pp. 419–424 (2003)Google Scholar
  9. 9.
    Lin, S., Kernighan, B.: An effective heuristic algorithm for the traveling salesman problem. Operations Research 21, 498–516 (1973)Google Scholar
  10. 10.
    Melo, L.A., Pereira, F.B., Costa, E.: MC-ANT: A multi-colony ant algorithm. In: Proc. 9th International Conference on Artificial Evolution, EA 2009, (LNCS 5975), pp. 25–36 (2010)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Kengo Katayama
    • 1
    Email author
  • Yusuke Okamoto
    • 1
  • Elis Kulla
    • 1
  • Noritaka Nishihara
    • 1
  1. 1.Department of Information and Computer EngineeringOkayama University of ScienceOkayamaJapan

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