Advanced Particle Swarm Optimization Algorithm with Improved Velocity Update Strategy

Khan, TA; Ling, SH; Mohan, AS

Advanced Particle Swarm Optimization Algorithm with Improved Velocity Update Strategy

Khan, TA Ling, SH

Mohan, AS

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Publication Type:: Conference Proceeding
Citation:: Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018, 2019, pp. 3944 - 3949
Issue Date:: 2019-01-16

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Field	Value	Language
dc.contributor.author	Khan, TA	en_US
dc.contributor.author	Ling, SH https://orcid.org/0000-0003-0849-5098	en_US
dc.contributor.author	Mohan, AS https://orcid.org/0000-0002-4503-5851	en_US
dc.date.available	2020-05-25T19:26:09Z
dc.date.issued	2019-01-16	en_US
dc.identifier.citation	Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018, 2019, pp. 3944 - 3949	en_US
dc.identifier.isbn	9781538666500	en_US
dc.identifier.uri	http://hdl.handle.net/10453/131668
dc.description.abstract	© 2018 IEEE. In this paper, advanced particle swarm optimization Algorithm (APSO) with improved velocity updated strategy is presented. The algorithm incorporates an improved velocity update equation so that the particles will reach the optimum point quickly and convergence is much faster than the standard PSO (SPSO) and other improved PSOs in the literature. Five benchmark functions have been selected to evaluate the efficiency of the proposed algorithm. The simulation results demonstrate that the proposed technique has remarkably improved in terms of convergence and solution quality.	en_US
dc.relation.ispartof	Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018	en_US
dc.relation.isbasedon	10.1109/SMC.2018.00669	en_US
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Advanced Particle Swarm Optimization Algorithm with Improved Velocity Update Strategy	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Biomedical Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
utslib.copyright.status	open_access	*
pubs.publication-status	Published	en_US

Abstract:

© 2018 IEEE. In this paper, advanced particle swarm optimization Algorithm (APSO) with improved velocity updated strategy is presented. The algorithm incorporates an improved velocity update equation so that the particles will reach the optimum point quickly and convergence is much faster than the standard PSO (SPSO) and other improved PSOs in the literature. Five benchmark functions have been selected to evaluate the efficiency of the proposed algorithm. The simulation results demonstrate that the proposed technique has remarkably improved in terms of convergence and solution quality.

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http://hdl.handle.net/10453/131668