Hybrid optimisation method using PGA and SQP algorithm

Skinner, BT; Nguyen, HT; Liu, DK

Hybrid optimisation method using PGA and SQP algorithm

Skinner, BT

Nguyen, HT

Liu, DK

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the 2007 IEEE Symposium on Foundations of Computational Intelligence, FOCI 2007, 2007, pp. 73 - 80
Issue Date:: 2007-09-27

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Field	Value	Language
dc.contributor.author	Skinner, BT https://orcid.org/0000-0002-7869-4458	en_US
dc.contributor.author	Nguyen, HT https://orcid.org/0000-0003-3373-8178	en_US
dc.contributor.author	Liu, DK https://orcid.org/0000-0002-1581-5582	en_US
dc.date.issued	2007-09-27	en_US
dc.identifier.citation	Proceedings of the 2007 IEEE Symposium on Foundations of Computational Intelligence, FOCI 2007, 2007, pp. 73 - 80	en_US
dc.identifier.isbn	1424407036	en_US
dc.identifier.isbn	9781424407033	en_US
dc.identifier.uri	http://hdl.handle.net/10453/2575
dc.description.abstract	This paper investigates the hybridisation of two very different optimisation methods, namely the Parallel Genetic Algorithm (PGA) and Sequential Quadratic Programming (SQP) Algorithm. The different characteristics of genetic-based and traditional quadratic programming-based methods are discussed and to what extent the hybrid method can benefit the solving of optimisation problems with nonlinear complex objective and constraint functions. Experiments show the hybrid method effectively combines the robust and global search property of Parallel Genetic Algorithms with the high convergence velocity of the Sequential Quadratic Programming Algorithm, thereby reducing computation time, maintaining robustness and increasing solution quality. © 2007 IEEE.	en_US
dc.relation.ispartof	Proceedings of the 2007 IEEE Symposium on Foundations of Computational Intelligence, FOCI 2007	en_US
dc.relation.isbasedon	10.1109/FOCI.2007.372150	en_US
dc.title	Hybrid optimisation method using PGA and SQP algorithm	en_US
dc.type	Conference Proceeding
utslib.for	0906 Electrical and Electronic Engineering	en_US
dc.location.activity	Honolulu, Hawaii, USA	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 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 - CHT - Health Technologies
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

This paper investigates the hybridisation of two very different optimisation methods, namely the Parallel Genetic Algorithm (PGA) and Sequential Quadratic Programming (SQP) Algorithm. The different characteristics of genetic-based and traditional quadratic programming-based methods are discussed and to what extent the hybrid method can benefit the solving of optimisation problems with nonlinear complex objective and constraint functions. Experiments show the hybrid method effectively combines the robust and global search property of Parallel Genetic Algorithms with the high convergence velocity of the Sequential Quadratic Programming Algorithm, thereby reducing computation time, maintaining robustness and increasing solution quality. © 2007 IEEE.

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http://hdl.handle.net/10453/2575