A Novel Ranking-based Optimal Guides Selection Strategy in MOPSO

Li, L; Wang, W; Li, W; Xu, X; Zhao, Y

A Novel Ranking-based Optimal Guides Selection Strategy in MOPSO

Li, L Wang, W Li, W Xu, X Zhao, Y

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Publication Type:: Journal Article
Citation:: Procedia Computer Science, 2016, 91 pp. 1001 - 1010
Issue Date:: 2016-01-01

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Field	Value	Language
dc.contributor.author	Li, L	en_US
dc.contributor.author	Wang, W	en_US
dc.contributor.author	Li, W	en_US
dc.contributor.author	Xu, X	en_US
dc.contributor.author	Zhao, Y	en_US
dc.date.issued	2016-01-01	en_US
dc.identifier.citation	Procedia Computer Science, 2016, 91 pp. 1001 - 1010	en_US
dc.identifier.issn	1877-0509	en_US
dc.identifier.uri	http://hdl.handle.net/10453/120338
dc.description.abstract	© 2016 Published by Elsevier B.V. A challenging issue with multi-objective particle swarm optimization (MOPSO) is the mechanism to select the optimal guides. This paper presents a new strategy based on ranking dominance and integrates into MOPSO. By using the ranking information and incorporating the chebychev distance of particle in objective space, we implement the selection of gbest and pbest simply and elegantly. On the basis of ranking, we propose a new maintenance strategy for updating the external archive which can obtain a more diverse and uniform distribution. Furthermore, a qualitative and quantitative analysis in terms of convergence analysis over some benchmarks is presented, providing a basis for conclusions about the proposed method. showing that the proposed method performs better than the adopted algorithms.	en_US
dc.relation.ispartof	Procedia Computer Science	en_US
dc.relation.isbasedon	10.1016/j.procs.2016.07.135	en_US
dc.rights	info:eu-repo/semantics/openAccess
dc.title	A Novel Ranking-based Optimal Guides Selection Strategy in MOPSO	en_US
dc.type	Journal Article
utslib.citation.volume	91	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	08 Information And Computing Sciences	en_US
utslib.for	10 Technology	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 Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - GEVI - Green Energy and Vehicle Innovations
utslib.copyright.status	open_access	*
pubs.declined	2017-11-20T10:56:45.927+1100
pubs.consider-herdc	false	en_US
pubs.publication-status	Published	en_US
pubs.volume	91	en_US

Abstract:

© 2016 Published by Elsevier B.V. A challenging issue with multi-objective particle swarm optimization (MOPSO) is the mechanism to select the optimal guides. This paper presents a new strategy based on ranking dominance and integrates into MOPSO. By using the ranking information and incorporating the chebychev distance of particle in objective space, we implement the selection of gbest and pbest simply and elegantly. On the basis of ranking, we propose a new maintenance strategy for updating the external archive which can obtain a more diverse and uniform distribution. Furthermore, a qualitative and quantitative analysis in terms of convergence analysis over some benchmarks is presented, providing a basis for conclusions about the proposed method. showing that the proposed method performs better than the adopted algorithms.

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http://hdl.handle.net/10453/120338