Intelligent fuzzy particle swarm optimization with cross-mutated operation

Ling, SH; Nguyen, HT; Leung, FHF; Chan, KY; Jiang, F

Intelligent fuzzy particle swarm optimization with cross-mutated operation

Ling, SH

Nguyen, HT

Leung, FHF Chan, KY Jiang, F

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Publication Type:: Conference Proceeding
Citation:: 2012 IEEE Congress on Evolutionary Computation, CEC 2012, 2012
Issue Date:: 2012-10-04

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Field	Value	Language
dc.contributor.author	Ling, SH https://orcid.org/0000-0003-0849-5098	en_US
dc.contributor.author	Nguyen, HT https://orcid.org/0000-0003-3373-8178	en_US
dc.contributor.author	Leung, FHF	en_US
dc.contributor.author	Chan, KY	en_US
dc.contributor.author	Jiang, F	en_US
dc.date.issued	2012-10-04	en_US
dc.identifier.citation	2012 IEEE Congress on Evolutionary Computation, CEC 2012, 2012	en_US
dc.identifier.isbn	9781467315098	en_US
dc.identifier.uri	http://hdl.handle.net/10453/23006
dc.description.abstract	This paper presents a novel fuzzy particle swarm optimization with cross-mutated operation (FPSOCM), where a fuzzy logic is applied to determine the inertia weight of PSO and the control parameter of the proposed cross-mutated operation based on human knowledge. By introducing the fuzzy system, the value of the inertia weight of PSO becomes adaptive. The new cross-mutated operation effectively drives the solution to escape from local optima. To illustrate the performance of the FPSOCM, a suite of benchmark test functions are employed. Experimental results show the proposed FPSOCMmethod performs better than some existing hybrid PSO methods in terms of solution quality and solution reliability (standard deviation upon many trials). Moreover, an industrial application of economic load dispatch is given to show that the FPSOCM method performs statistically more significant than the existing hybrid PSO methods. © 2012 IEEE.	en_US
dc.relation.ispartof	2012 IEEE Congress on Evolutionary Computation, CEC 2012	en_US
dc.relation.isbasedon	10.1109/CEC.2012.6252934	en_US
dc.title	Intelligent fuzzy particle swarm optimization with cross-mutated operation	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	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 Biomedical Engineering
pubs.organisational-group	/University of Technology Sydney/Provost
pubs.organisational-group	/University of Technology Sydney/Provost/Jumbunna
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

This paper presents a novel fuzzy particle swarm optimization with cross-mutated operation (FPSOCM), where a fuzzy logic is applied to determine the inertia weight of PSO and the control parameter of the proposed cross-mutated operation based on human knowledge. By introducing the fuzzy system, the value of the inertia weight of PSO becomes adaptive. The new cross-mutated operation effectively drives the solution to escape from local optima. To illustrate the performance of the FPSOCM, a suite of benchmark test functions are employed. Experimental results show the proposed FPSOCMmethod performs better than some existing hybrid PSO methods in terms of solution quality and solution reliability (standard deviation upon many trials). Moreover, an industrial application of economic load dispatch is given to show that the FPSOCM method performs statistically more significant than the existing hybrid PSO methods. © 2012 IEEE.

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http://hdl.handle.net/10453/23006