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Dispersive Flies Optimisation

Dispersive Flies Optimisation

Al-Rifaie, Mohammad Majid ORCID: 0000-0002-1798-9615 (2014) Dispersive Flies Optimisation. In: Proceedings of the 2014 Federated Conference on Computer Science and Information Systems. Annals of Computer Science and Information Systems, 2 . FedCSIS, pp. 529-538. ISBN 978-8360810583 ISSN 2300-5963 (Print), 2300-5963 (Online) (doi:

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One of the main sources of inspiration for techniques applicable to complex search space and optimisation problems is nature. This paper proposes a new metaheuristic - Dispersive Flies Optimisation or DFO - whose inspiration is beckoned from the swarming behaviour of flies over food sources in nature. The simplicity of the algorithm, which is the implementation of one such paradigm for continuous optimisation, facilitates the analysis of its behaviour. A series of experimental trials confirms the promising performance of the optimiser over a set of benchmarks, as well as its competitiveness when compared against three other well-known population based algorithms (Particle Swarm Optimisation, Differential Evolution algorithm and Genetic Algorithm). The convergence-independent diversity of DFO algorithm makes it a potentially suitable candidate for dynamically changing environment. In addition to diversity, the performance of the newly introduced algorithm is investigated using the three performance measures of accuracy, efficiency and reliability and its outperformance is demonstrated in the paper.

Item Type: Conference Proceedings
Title of Proceedings: Proceedings of the 2014 Federated Conference on Computer Science and Information Systems
Uncontrolled Keywords: Swarm intelligence, optimisation, dispersive flies optimisation, diversity, evolutionary systems
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty / Department / Research Group: Faculty of Liberal Arts & Sciences
Faculty of Liberal Arts & Sciences > Centre for Computer & Computational Science
Faculty of Liberal Arts & Sciences > School of Computing & Mathematical Sciences (CAM)
Last Modified: 26 Nov 2020 22:35
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
Selected for REF2021: None

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