Dispersive Flies Optimisation (DFO)

Summary

Swarm behaviour in Dispersive Flies Optimisation

Dispersive flies optimisation (DFO) is a bare-bones swarm intelligence algorithm which is inspired by the swarming behaviour of flies hovering over food sources. DFO is a simple optimiser which works by iteratively trying to improve a candidate solution with regard to a numerical measure that is calculated by a fitness function. Each member of the population, a fly or an agent, holds a candidate solution whose suitability can be evaluated by their fitness value. Optimisation problems are often formulated as either minimisation or maximisation problems.

DFO was introduced with the intention of analysing a simplified swarm intelligence algorithm with the fewest tunable parameters and components. In the first work on DFO, this algorithm was compared against a few other existing swarm intelligence techniques using error, efficiency and diversity measures. It is shown that despite the simplicity of the algorithm, which only uses agents’ position vectors at time t to generate the position vectors for time t + 1, it exhibits a competitive performance. Since its inception, DFO has been used in a variety of applications including medical imaging and image analysis as well as data mining and machine learning.

Algorithm

Resources

1. Wikipedia

Citations

1. Al-Rifaie, Mohammad Majid. "Dispersive Flies Optimisation." In 2014 federated conference on computer science and information systems, pp. 529-538. IEEE, 2014.

   @inproceedings{al2014dispersive,
       title={Dispersive flies optimisation},
       author={Al-Rifaie, Mohammad Majid},
       booktitle={2014 federated conference on computer science and information systems},
       pages={529--538},
       year={2014},
       organization={IEEE}
   }
   

2. Aparajeya, Prashant, Frederic Fol Leymarie, and Mohammad Majid Al-Rifaie. "Swarm-based identification of animation key points from 2d-medialness maps." In Computational Intelligence in Music, Sound, Art and Design: 8th International Conference, EvoMUSART 2019, Held as Part of EvoStar 2019, Leipzig, Germany, April 24–26, 2019, Proceedings 8, pp. 69-83. Springer International Publishing, 2019.

   @inproceedings{aparajeya2019swarm,
       title={Swarm-based identification of animation key points from 2d-medialness maps},
       author={Aparajeya, Prashant and Leymarie, Frederic Fol and Al-Rifaie, Mohammad Majid},
       booktitle={Computational Intelligence in Music, Sound, Art and Design: 8th International Conference, EvoMUSART 2019, Held as Part of EvoStar 2019, Leipzig, Germany, April 24--26, 2019, Proceedings 8},
       pages={69--83},
       year={2019},
       organization={Springer}
   }
   

3. Hooman, Oroojeni MJ, Mohammad Majid Al-Rifaie, and Mihalis A. Nicolaou. "Deep neuroevolution: Training deep neural networks for false alarm detection in intensive care units." In 2018 26th European Signal Processing Conference (EUSIPCO), pp. 1157-1161. IEEE, 2018.

   @inproceedings{hooman2018deep,
       title={Deep neuroevolution: Training deep neural networks for false alarm detection in intensive care units},
       author={Hooman, Oroojeni MJ and Al-Rifaie, Mohammad Majid and Nicolaou, Mihalis A},
       booktitle={2018 26th European Signal Processing Conference (EUSIPCO)},
       pages={1157--1161},
       year={2018},
       organization={IEEE}
   }