An enhanced particle swarm optimization algorithm for multi-modal functions

Kwok, NM; Fang, G; Ha, QR; Liu, DK

An enhanced particle swarm optimization algorithm for multi-modal functions

Kwok, NM Fang, G Ha, QR

Liu, DK

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the 2007 IEEE International Conference on Mechatronics and Automation, ICMA 2007, 2007, pp. 457 - 462
Issue Date:: 2007-12-18

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Field	Value	Language
dc.contributor.author	Kwok, NM	en_US
dc.contributor.author	Fang, G	en_US
dc.contributor.author	Ha, QR https://orcid.org/0000-0003-0978-1758	en_US
dc.contributor.author	Liu, DK https://orcid.org/0000-0002-1581-5582	en_US
dc.date.issued	2007-12-18	en_US
dc.identifier.citation	Proceedings of the 2007 IEEE International Conference on Mechatronics and Automation, ICMA 2007, 2007, pp. 457 - 462	en_US
dc.identifier.isbn	1424408288	en_US
dc.identifier.isbn	9781424408283	en_US
dc.identifier.uri	http://hdl.handle.net/10453/2857
dc.description.abstract	The particle swarm optimization algorithm has been frequently employed to solve various optimization problems. Although the algorithm is performing satisfactorily while tackling unit-modal optimizations, enhancements in dealing with multi-modal functions are indeed desirable. Convergence of particles to the optimum solution is a primary and traditional requirement, however, this is achieved only after all the solutions space has been covered and evaluated. In this work, the focus is directed towards maintaining sufficient divergence of particles in multi-modal problems, by developing an alternative social interaction scheme among the swarm members. Particularly, a multiple-leaders strategy is employed in the new PSO algorithm to prevent pre-mature convergence. Results from benchmark problems are included to illustrate the effectiveness of the proposed method. © 2007 IEEE.	en_US
dc.relation.ispartof	Proceedings of the 2007 IEEE International Conference on Mechatronics and Automation, ICMA 2007	en_US
dc.relation.isbasedon	10.1109/ICMA.2007.4303586	en_US
dc.title	An enhanced particle swarm optimization algorithm for multi-modal functions	en_US
dc.type	Conference Proceeding
utslib.for	0910 Manufacturing Engineering	en_US
utslib.for	0913 Mechanical Engineering	en_US
dc.location.activity	Harbin, Heilongjiang, China	en_US
dc.location.activity	Harbin, PEOPLES R CHINA
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/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CAS - Centre for Autonomous Systems
pubs.organisational-group	/University of Technology Sydney/Strength - CBI - Centre for Built Infrastructure
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

The particle swarm optimization algorithm has been frequently employed to solve various optimization problems. Although the algorithm is performing satisfactorily while tackling unit-modal optimizations, enhancements in dealing with multi-modal functions are indeed desirable. Convergence of particles to the optimum solution is a primary and traditional requirement, however, this is achieved only after all the solutions space has been covered and evaluated. In this work, the focus is directed towards maintaining sufficient divergence of particles in multi-modal problems, by developing an alternative social interaction scheme among the swarm members. Particularly, a multiple-leaders strategy is employed in the new PSO algorithm to prevent pre-mature convergence. Results from benchmark problems are included to illustrate the effectiveness of the proposed method. © 2007 IEEE.

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http://hdl.handle.net/10453/2857