HIDMS-PSO with bio-inspired fission-fusion behaviour and a quorum decision mechanism

Varna, Fevzi Tugrul and Husbands, Phil (2021) HIDMS-PSO with bio-inspired fission-fusion behaviour and a quorum decision mechanism. IEEE CEC 2021, Kraków, Poland, 28 Jun - 1 Jul 2021. Published in: Proceedings of IEEE Congress on Evolutionary Computation (CEC). 1398-1405. IEEE ISBN 9781728183947

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In this study, we propose a new variant of the HIDMS-PSO algorithm with a bio-inspired fission-fusion behaviour and a quorum decision mechanism (FFQ-HIDMS-PSO). In the new algorithm, units are conceptualised as self-organising fission-fusion societies that determine and adopt a suitable behaviour using unit-based quorum decisions. The incorporation of the two bio-inspired mechanisms provide "diversity aware" self-organising units that react to stagnation of particles by adopting a suitable fission-fusion behaviour, leading to a more efficient algorithm capable of maintaining significantly better population diversity throughout the search. The performance of the proposed algorithm was verified with three distinct experiments conducted using CEC’17 and CEC’05 test suites at 30 and 50 dimensions, comparing against 12 state-of-the-art metaheuristics and 12 state-of-the-art PSO variants. The proposed algorithm showed superior performance in these experiments by outperforming all 24 algorithms in all three experiments at 30 and 50 dimensions. The empirical evidence suggests that the proposed method also maintains significantly superior population diversity in comparison to the original HIDMS-PSO.

Item Type: Conference Proceedings
Additional Information: © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works
Keywords: particle swarm optimisation, meatheuristics, AI
Schools and Departments: School of Engineering and Informatics > Informatics
Research Centres and Groups: Centre for Computational Neuroscience and Robotics
SWORD Depositor: Mx Elements Account
Depositing User: Mx Elements Account
Date Deposited: 19 Aug 2021 07:44
Last Modified: 04 Mar 2022 17:23
URI: http://sro.sussex.ac.uk/id/eprint/101184

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