Experimental study of a new hybrid PSO with mutation for economic dispatch with non-smooth cost function

Lu, H; Sriyanyong, P; Song, YH; Dillon, T

Experimental study of a new hybrid PSO with mutation for economic dispatch with non-smooth cost function

Lu, H

Sriyanyong, P Song, YH Dillon, T

Permalink

Publication Type:: Journal Article
Citation:: International Journal of Electrical Power and Energy Systems, 2010, 32 (9), pp. 921 - 935
Issue Date:: 2010-11-01

Closed Access

	Filename	Description	Size
	2009007495OK.pdf		1.19 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Lu, H https://orcid.org/0000-0001-5655-0237	en_US
dc.contributor.author	Sriyanyong, P	en_US
dc.contributor.author	Song, YH	en_US
dc.contributor.author	Dillon, T	en_US
dc.date.issued	2010-11-01	en_US
dc.identifier.citation	International Journal of Electrical Power and Energy Systems, 2010, 32 (9), pp. 921 - 935	en_US
dc.identifier.issn	0142-0615	en_US
dc.identifier.uri	http://hdl.handle.net/10453/13593
dc.description.abstract	Particle swarm optimization (PSO) is a population-based evolutionary technique. Advancements in the PSO development over the last decade have made it one of the most promising optimization algorithms for a wide range of complex engineering optimization problems which traditional derivative-based optimization techniques cannot handle. The most attractive features of PSO are its algorithmic simplicity and fast convergence. However, PSO tends to suffer from premature convergence when applied to strongly multi-modal optimization problems. This paper proposes a method of incorporating a real-valued mutation (RVM) operator into the PSO algorithms, aimed at enhancing global search capability. Three variants of PSO algorithms are considered. The resultant hybrid PSO-RVM algorithms are experimentally investigated along with the PSO variants and an existing PSO with Gaussian mutation using six typical benchmark functions. It is interesting to see that the effectiveness of RVM varies for different PSO variants as well as different kinds of functions. It has been found that one of the hybrid algorithms, CBPSO-RVM, which is an integration of the PSO with the constriction factor and inertia weight (CBPSO) and the RVM operator, exhibits significantly better performance in most of the test cases compared to the other algorithms under consideration. Furthermore, this algorithm is superior to most of the existing algorithms used in this study when applied to two practical ED problems with non-smooth cost function considering the multiple fuel type and/or valve-point loading effects. © 2010 Elsevier Ltd.	en_US
dc.relation.ispartof	International Journal of Electrical Power and Energy Systems	en_US
dc.relation.isbasedon	10.1016/j.ijepes.2010.03.001	en_US
dc.subject.classification	Energy	en_US
dc.title	Experimental study of a new hybrid PSO with mutation for economic dispatch with non-smooth cost function	en_US
dc.type	Journal Article
utslib.citation.volume	9	en_US
utslib.citation.volume	32	en_US
utslib.for	0906 Electrical and Electronic Engineering	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/Faculty of Engineering and Information Technology/School of Software
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.issue	9	en_US
pubs.publication-status	Published	en_US
pubs.volume	32	en_US

Abstract:

Particle swarm optimization (PSO) is a population-based evolutionary technique. Advancements in the PSO development over the last decade have made it one of the most promising optimization algorithms for a wide range of complex engineering optimization problems which traditional derivative-based optimization techniques cannot handle. The most attractive features of PSO are its algorithmic simplicity and fast convergence. However, PSO tends to suffer from premature convergence when applied to strongly multi-modal optimization problems. This paper proposes a method of incorporating a real-valued mutation (RVM) operator into the PSO algorithms, aimed at enhancing global search capability. Three variants of PSO algorithms are considered. The resultant hybrid PSO-RVM algorithms are experimentally investigated along with the PSO variants and an existing PSO with Gaussian mutation using six typical benchmark functions. It is interesting to see that the effectiveness of RVM varies for different PSO variants as well as different kinds of functions. It has been found that one of the hybrid algorithms, CBPSO-RVM, which is an integration of the PSO with the constriction factor and inertia weight (CBPSO) and the RVM operator, exhibits significantly better performance in most of the test cases compared to the other algorithms under consideration. Furthermore, this algorithm is superior to most of the existing algorithms used in this study when applied to two practical ED problems with non-smooth cost function considering the multiple fuel type and/or valve-point loading effects. © 2010 Elsevier Ltd.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/13593