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Swarm Optimised Few-View Binary Tomography

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Applications of Evolutionary Computation (EvoApplications 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13224))

Abstract

This paper considers a swarm optimisation approach to few-view tomographic reconstruction. DFOMAX, a high diversity swarm optimiser, demonstrably reconstructs binary images to a high fidelity, outperforming a leading algebraic technique, differential evolution and particle swarm optimisation on four standard phantoms. The paper considers the effectiveness of optimisers that have been developed for optimal low dimensional performance and concludes that trial solution clamping on the walls of the feasible search space is important for good performance.

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Notes

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Acknowledgement

The authors would like to thank Darren Wise for his support in facilitating access to the HPC machines at the University of Greenwich.

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Authors and Affiliations

  1. Univeristy of Greenwich, London, SE10 9LS, UK

    Mohammad Majid al-Rifaie

  2. Goldsmiths, University of London, London, SE14 6NW, UK

    Tim Blackwell

Authors
  1. Mohammad Majid al-Rifaie
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  2. Tim Blackwell
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Correspondence to Mohammad Majid al-Rifaie .

Editor information

Editors and Affiliations

  1. Université Le Havre Normandie, Le Havre, France

    Juan Luis Jiménez Laredo

  2. Universidad Complutense de Madrid, Madrid, Spain

    J. Ignacio Hidalgo

  3. Edinburgh Napier University, Edinburgh, UK

    Kehinde Oluwatoyin Babaagba

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al-Rifaie, M.M., Blackwell, T. (2022). Swarm Optimised Few-View Binary Tomography. In: Jiménez Laredo, J.L., Hidalgo, J.I., Babaagba, K.O. (eds) Applications of Evolutionary Computation. EvoApplications 2022. Lecture Notes in Computer Science, vol 13224. Springer, Cham. https://doi.org/10.1007/978-3-031-02462-7_3

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  • DOI: https://doi.org/10.1007/978-3-031-02462-7_3

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  • Online ISBN: 978-3-031-02462-7

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