Al-Rifaie, Mohammad Majid ORCID: 0000-0002-1798-9615 (2021) Investigating knowledge-based exploration-exploitation balance in a minimalist swarm optimiser. In: 2021 IEEE Congress on Evolutionary Computation (CEC). Poland, Krakow. 28.06-.01.07. 2021. IEEExplore . Institute of Electrical and Electronics Engineers (IEEE), Piscataway, New Jersey. US, pp. 2273-2280. ISBN 978-1728183930/21 ; 978-1-7281-8393-0 ; 978-1-7281-8392-3 ; 978-1-7281-8394-7 (doi:https://doi.org/10.1109/CEC45853.2021.9504805)

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Abstract

One of the key challenges in evolutionary, swarm and population-based optimisers is the balance between exploration and exploitation. The reliance on both guided and stochastic search in these algorithms allows researchers to take different approaches to the topic. This work uses a minimalist, vector-stripped swarm optimiser to present a theoretical analysis on the behaviour of the particles. Being a population based continuous optimiser, dispersive flies optimisation or DFO, bears several similarities with the well-known particle swarm optimisers, differential evolution algorithms and their bare-bones variants. The distinctive feature of this algorithm is its sheer reliance on particles positions to update the population. The minimalist nature of the algorithm reduces the challenges of understanding particles oscillation around constantly changing centres, particles’ influence on one another, and their trajectories. This work presents a unified exploration-exploitation probability study which is derived from six scenarios in order to examine the population’s dimensional behaviour in each iteration. This paves the way to propose and investigate adaptable, diversity promoting mechanisms. The proposed methods, which may be extendable to other optimisers, are then examined on a comprehensive set of benchmarks, and finally applied to high dimensional tomographic reconstruction which is an important inverse problem in medical and industrial imaging.

Item Type:	Conference Proceedings
Title of Proceedings:	2021 IEEE Congress on Evolutionary Computation (CEC). Poland, Krakow. 28.06-.01.07. 2021
Uncontrolled Keywords:	exploration; exploitation; diversity; dispersive flies optimisation; DFO
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty / School / Research Centre / Research Group:	Faculty of Engineering & Science Faculty of Engineering & Science > School of Computing & Mathematical Sciences (CMS) Faculty of Liberal Arts & Sciences > Computational Science & Engineering Group (CSEH)
Related URLs:	Publisher
Last Modified:	04 Mar 2022 16:35
URI:	http://gala.gre.ac.uk/id/eprint/33572

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