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Multi-population Genetic Algorithms with Immigrants Scheme for Dynamic Shortest Path Routing Problems in Mobile Ad Hoc Networks

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Book cover Applications of Evolutionary Computation (EvoApplications 2010)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6024))

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Abstract

The static shortest path (SP) problem has been well addressed using intelligent optimization techniques, e.g., artificial neural networks, genetic algorithms (GAs), particle swarm optimization, etc. However, with the advancement in wireless communications, more and more mobile wireless networks appear, e.g., mobile ad hoc network (MANET), wireless mesh network, etc. One of the most important characteristics in mobile wireless networks is the topology dynamics, that is, the network topology changes over time due to energy conservation or node mobility. Therefore, the SP problem turns out to be a dynamic optimization problem in mobile wireless networks. In this paper, we propose to use multi-population GAs with immigrants scheme to solve the dynamic SP problem in MANETs which is the representative of new generation wireless networks. The experimental results show that the proposed GAs can quickly adapt to the environmental changes (i.e., the network topology change) and produce good solutions after each change.

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Authors and Affiliations

  1. Department of Computer Science, University of Leicester, University Road, Leicester, LE1 7RH, United Kingdom

    Hui Cheng & Shengxiang Yang

Authors
  1. Hui Cheng
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  2. Shengxiang Yang
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Editor information

Editors and Affiliations

  1. Department of Mathematics and Statistics, University of Strathclyde, 16 Richmond Street, G1 1XQ, Glasgow, UK

    Cecilia Di Chio

  2. Department of Computer Engineering, University of Parma, Viale Usberti 181/a, 43100, Parma, Italy

    Stefano Cagnoni

  3. Departmento Lenguajes y Ciencias de la Computación, ETSI Informática, Universidad de Málaga, Campus Teatinos, 29071, Málaga, Spain

    Carlos Cotta

  4. Wilhelm Schickard-Institut für Informatik, Sand 1, Universität Tübingen, 72076, Tübingen, Germany

    Marc Ebner

  5. Computer Science, Aston Triangle, Aston University, B4 7ET, Birmingham, UK

    Anikó Ekárt

  6. Instituto Tecnológico de Informática, Universidad Politécnica de Valencia, Edif. 8G Acceso B, 46022, Valencia, Spain

    Anna I. Esparcia-Alcazar

  7. Advanced Technology Centre Rolls-Royce, Singapore, Singapore

    Chi-Keong Goh

  8. Departamento de Electrónica y Tnologia de los Computadores, University of Granada, 18071, Grenada, Spain

    Juan J. Merelo

  9. Department of Mathematical Information Technology,, University of Jyväskylä, 4th Floor, P.O. Box 25, 40014, Agora, Finland

    Ferrante Neri

  10. Lehrstuhl für Algorithm Engineering, TU Dortmund, Otto-Hahn-Straße 14, 44227, Dortmund, Germany

    Mike Preuß

  11. Center for Computer Games Research, IT University of Copenhagen, Rued Langgaards Vej 7, 2300, Copenhagen S, Denmark

    Julian Togelius  & Georgios N. Yannakakis  & 

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Cheng, H., Yang, S. (2010). Multi-population Genetic Algorithms with Immigrants Scheme for Dynamic Shortest Path Routing Problems in Mobile Ad Hoc Networks. In: Di Chio, C., et al. Applications of Evolutionary Computation. EvoApplications 2010. Lecture Notes in Computer Science, vol 6024. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12239-2_58

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  • DOI: https://doi.org/10.1007/978-3-642-12239-2_58

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-12238-5

  • Online ISBN: 978-3-642-12239-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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