Dispersive Flies Optimisation

Snapshot during the talk

Dispersive Flies Optimisation (DFO) is a global optimising algorithm inspired by the swarming behaviour of flies hovering over food sources. As detailed in the original paper, the swarming behaviour of flies is determined by several factors and that the presence of threat could disturb their convergence on the marker (or the optimum value). Therefore, having considered the formation of the swarms over the marker, the breaking or weakening of the swarms is noted in the proposed algorithm. Therefore, the swarming behaviour of the flies, in Dispersive Flies Optimisation, consist of two tightly connected mechanisms, one is the formation of the swarms and the other is its breaking or weakening.





Copyright (C) 2014 Mohammad Majid al-Rifaie
This is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License.

For any query contact:
m.majid@gold.ac.uk

Department of Computing
Goldsmiths, University of London
London SE14 6NW, United Kingdom

How to reference?

Mohammad Majid Al-Rifaie (2014), Dispersive Flies Optimisation, Proceedings of the 2014 Federated Conference on Computer Science and Information Systems, 535--544. IEEE.

Source code

Click here to download the source code in Java.

If you are interested in visualising the behaviour of the flies during the optimisation process, the relevant source code can be downloaded here.

Below you can see the videos demonstrating the behaviour of DFO flies optimising 10 dimensional Sphere and Rastrigin functions:


Relevant papers

  1. [Paper] Aparajeya, P., Leymarie, F. F., & al-Rifaie, M. M. (2019, April). Swarm-based identification of animation key points from 2D-medialness maps. In International Conference on Computational Intelligence in Music, Sound, Art and Design (EvoMUSART), pp. 69-83. Springer, Cham. Leipzig, Germany, April 24–26 [Best Paper].

  2. [Paper] Oroojeni M. J., H., al-Rifaie, M. M., Nicolaou, M. A. (2018), Deep Neuroevolution: Training Deep Neural Networks for False Alarm Detection in Intensive Care Units, 26th European Signal Processing Conference, IEEE (accepted and in press)

  3. [Thesis] Alhakbani, H. (2018). Handling class imbalance using swarm intelligence techniques, hybrid data and algorithmic level solutions (Doctoral dissertation, Ph. D. thesis, Goldsmiths, University of London, London, United Kingdom).

  4. [Paper] al-Rifaie, M. M., Ursyn, A., Zimmer, R., & Javaheri Javid, M. A. (2017). On Symmetry, Aesthetics and Quantifying Symmetrical Complexity. In Computational Intelligence in Music, Sound, Art and Design: 6th International Conference, EvoMUSART 2017, Amsterdam, Netherlands, April 19–21, 2017, Proceedings (Vol. 10198, p. 17-32). Springer.

  5. [Paper] King, M. and al-Rifaie, M. M., (2017), Building Simple Non-identical Organic Structures with Dispersive Flies Optimisation and A* Path-finding. In Proc. AISB 2017: 8th Symposium on AI & Games, Bath, UK.

  6. [Paper] al-Rifaie, M.M., (2017), Perceived Simplicity and Complexity in Nature. In Proc. AISB 2017: Computational Architectures for Animal Cognition (CAAC), Bath, UK.

  7. [Journal Paper] al-Rifaie, M. M., Leymarie, F.L., Latham, W., and Bishop (2017), Swarmic Autopoiesis and Computational Creativity, In 2nd Special Issue on Computational Creativity, al-Rifaie, M. M., McGregor, S. (ed.), Connection Science, Taylor & Francis. DOI: 10.1080/09540091.2016.1274960 (accepted and in press)

  8. [Book chapter] al-Rifaie, M. M., Aber, A., (2016), Dispersive Flies Optimisation and Medical Imaging, Recent Advances in Computational Optimization, Studies in Computational Intelligence Volume 610, pp 183-203, S. Fidanova (ed), Studies in Computational Intelligence, Springer.