Fractal algorithms for finding global optimal solutions

Song, JL; He, X; Qian, FC

Fractal algorithms for finding global optimal solutions

Song, JL He, X

Qian, FC

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Publication Type:: Conference Proceeding
Citation:: Fourth International Conference on Information Technology and Applications, ICITA 2007, 2007, pp. 258 - 261
Issue Date:: 2007-12-03

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Field	Value	Language
dc.contributor.author	Song, JL	en_US
dc.contributor.author	He, X https://orcid.org/0000-0001-8962-540X	en_US
dc.contributor.author	Qian, FC	en_US
dc.date.issued	2007-12-03	en_US
dc.identifier.citation	Fourth International Conference on Information Technology and Applications, ICITA 2007, 2007, pp. 258 - 261	en_US
dc.identifier.isbn	0980326702	en_US
dc.identifier.isbn	9780980326703	en_US
dc.identifier.uri	http://hdl.handle.net/10453/2614
dc.description.abstract	For solving constrained nonlinear optimization problems, a new algorithm, which is called Fractal Algorithm, is presented. Feasible region is partitioned by fractal combining with golden section. Bad region is deleted, gradually and finally optimal solution remains. The advantages of the local fine structure of fractal and the quick convergence of golden section method are taken. Hence, the fractal algorithm is highly efficient and. highly speedy. The algorithm has the character of strong adaptability to a class of complex function. It requests only that the object function has one order derivative. The minimum can be found at any precision at which a computer can work. Furthermore, this method requests so little memory that it almost can. be implemented on any PC of which the efficiency is almost not influenced. The proof showing convergence of the algorithm, is given. The numerical results show that the algorithm is effective.	en_US
dc.relation.ispartof	Fourth International Conference on Information Technology and Applications, ICITA 2007	en_US
dc.title	Fractal algorithms for finding global optimal solutions	en_US
dc.type	Conference Proceeding
utslib.for	1702 Cognitive Sciences	en_US
dc.location.activity	Harbin, China	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 Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CRIN - Realtime Information Networks
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US

Abstract:

For solving constrained nonlinear optimization problems, a new algorithm, which is called Fractal Algorithm, is presented. Feasible region is partitioned by fractal combining with golden section. Bad region is deleted, gradually and finally optimal solution remains. The advantages of the local fine structure of fractal and the quick convergence of golden section method are taken. Hence, the fractal algorithm is highly efficient and. highly speedy. The algorithm has the character of strong adaptability to a class of complex function. It requests only that the object function has one order derivative. The minimum can be found at any precision at which a computer can work. Furthermore, this method requests so little memory that it almost can. be implemented on any PC of which the efficiency is almost not influenced. The proof showing convergence of the algorithm, is given. The numerical results show that the algorithm is effective.

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http://hdl.handle.net/10453/2614