Implementation and comparison of PSO-based algorithms for multi-modal optimization problems

Sriyanyong, P; Lu, H

Implementation and comparison of PSO-based algorithms for multi-modal optimization problems

Sriyanyong, P Lu, H

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Publication Type:: Conference Proceeding
Citation:: AIP Conference Proceedings, 2013, 1559 pp. 165 - 174
Issue Date:: 2013-11-25

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Field	Value	Language
dc.contributor.author	Sriyanyong, P	en_US
dc.contributor.author	Lu, H https://orcid.org/0000-0001-5655-0237	en_US
dc.date.issued	2013-11-25	en_US
dc.identifier.citation	AIP Conference Proceedings, 2013, 1559 pp. 165 - 174	en_US
dc.identifier.issn	0094-243X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/28376
dc.description.abstract	This paper aims to compare the global search capability and overall performance of a number of Particle Swarm Optimization (PSO) based algorithms in the context solving the Dynamic Economic Dispatch (DED) problem which takes into account the operation limitations of generation units such as valve-point loading effect as well as ramp rate limits. The comparative study uses six PSO-based algorithms including the basic PSO and hybrid PSO algorithms using a popular benchmark test IEEE power system which is 10-unit 24-hour system with non-smooth cost functions. The experimental results show that one of the hybrid algorithms that combines the PSO with both inertia weight and constriction factor, and the Gaussian mutation operator (CBPSO-GM) is promising in achieving the near global optimal of a non-linear multi-modal optimization problem, such as the DED problem under the consideration. © 2013 AIP Publishing LLC.	en_US
dc.relation.ispartof	AIP Conference Proceedings	en_US
dc.relation.isbasedon	10.1063/1.4825008	en_US
dc.title	Implementation and comparison of PSO-based algorithms for multi-modal optimization problems	en_US
dc.type	Conference Proceeding
utslib.citation.volume	1559	en_US
utslib.for	0802 Computation Theory and Mathematics	en_US
dc.location.activity	Sydney, Australia	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 Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	1559	en_US

Abstract:

This paper aims to compare the global search capability and overall performance of a number of Particle Swarm Optimization (PSO) based algorithms in the context solving the Dynamic Economic Dispatch (DED) problem which takes into account the operation limitations of generation units such as valve-point loading effect as well as ramp rate limits. The comparative study uses six PSO-based algorithms including the basic PSO and hybrid PSO algorithms using a popular benchmark test IEEE power system which is 10-unit 24-hour system with non-smooth cost functions. The experimental results show that one of the hybrid algorithms that combines the PSO with both inertia weight and constriction factor, and the Gaussian mutation operator (CBPSO-GM) is promising in achieving the near global optimal of a non-linear multi-modal optimization problem, such as the DED problem under the consideration. © 2013 AIP Publishing LLC.

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http://hdl.handle.net/10453/28376