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Biologically-Inspired Optimisation Methods pp 51–78Cite as

Asynchronous Multi-Objective Optimisation in Unreliable Distributed Environments

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 210))

Abstract

This chapter examines the performance characteristics of both asynchronous and synchronous parallel particle swarm optimisation algorithms in heterogeneous, fault-prone environments. The chapter starts with a simple parallelisation paradigm, the Master-Slave model using Multi-Objective Particle Swarm Optimisation (MOPSO) in a heterogeneous environment. Extending the investigation to general, distributed environments, algorithm convergence is measured as a function of both iterations completed and time elapsed. Asynchronous particle updates are shown to perform comparably to synchronous updates in fault-free environments. When faults are introduced, the synchronous update method is shown to suffer significant performance drops, suggesting that at least partly asynchronous algorithms should be used in real-world environments. Finally, the issue of how to utilise newly available nodes, as well as the loss of existing nodes, is considered and two methods of generating new particles during algorithm execution are investigated.

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Author information

Authors and Affiliations

  1. Institute for Integrated and Intelligent Systems, Griffith University, Brisbane Queensland, Australia

    Andrew Lewis

  2. Institute AIFB, University of Karlsruhe, Germany

    Sanaz Mostaghim

  3. School of Engineering, Griffith University, Brisbane Queensland, Australia

    Ian Scriven

Authors
  1. Andrew Lewis
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  2. Sanaz Mostaghim
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  3. Ian Scriven
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Editor information

Editors and Affiliations

  1. Institute for Integrated and Intelligent Systems, Griffith University, Nathan Campus, 4111, Brisbane, Queensland, Australia

    Andrew Lewis

  2. Institut für Angewandte Informatik und Formale Beschreibungsverfahren - AIFB, Universität Karlsruhe, 76128, Karlsruhe, Germany

    Sanaz Mostaghim

  3. Faculty of Business, Technology and Sustainable Development, Bond University, 4229, Gold Coast, Queensland, Australia

    Marcus Randall

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© 2009 Springer-Verlag Berlin Heidelberg

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Lewis, A., Mostaghim, S., Scriven, I. (2009). Asynchronous Multi-Objective Optimisation in Unreliable Distributed Environments. In: Lewis, A., Mostaghim, S., Randall, M. (eds) Biologically-Inspired Optimisation Methods. Studies in Computational Intelligence, vol 210. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01262-4_3

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  • DOI: https://doi.org/10.1007/978-3-642-01262-4_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-01261-7

  • Online ISBN: 978-3-642-01262-4

  • eBook Packages: EngineeringEngineering (R0)

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