A modified particle swarm optimization algorithm for solving DNA problem

Khan, TA; Ling, SH; Tram, N; Sanagavarapu, AM

A modified particle swarm optimization algorithm for solving DNA problem

Khan, TA Ling, SH

Tram, N Sanagavarapu, AM

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Publication Type:: Conference Proceeding
Citation:: 60th International Scientific Conference on Information Technology and Management Science of Riga Technical University, ITMS 2019 - Proceedings, 2019
Issue Date:: 2019-10-01

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Field	Value	Language
dc.contributor.author	Khan, TA	en_US
dc.contributor.author	Ling, SH https://orcid.org/0000-0003-0849-5098	en_US
dc.contributor.author	Tram, N	en_US
dc.contributor.author	Sanagavarapu, AM https://orcid.org/0000-0002-4503-5851	en_US
dc.date.issued	2019-10-01	en_US
dc.identifier.citation	60th International Scientific Conference on Information Technology and Management Science of Riga Technical University, ITMS 2019 - Proceedings, 2019	en_US
dc.identifier.isbn	9781728157092	en_US
dc.identifier.uri	http://hdl.handle.net/10453/138168
dc.description.abstract	© 2019 IEEE. DNA Sequencing is a complex problem since DNA computation depends on the biochemical reactions of DNA molecules that resulted in an improper or unwanted results. Thus, researchers are focusing to make the molecular computation for the DNA sequences design problem more consistent. Designing of DNA sequencing consists of several difficult and inconsistent designing parameters and typical optimization approaches do not perform well. As a result, a Modified Particle Swarm Optimization Algorithm (MPSO) is proposed to elucidate this problem. Four objective functions that are continuity, similarity, hairpin, and H-measure are used to evaluate this multi-objective problem. Different methods are used to explain DNA problem but to solve it with MPSO the multi-objective problem is converted into a single-objective problem. MPSO is presented to minimize the objective functions subject to two constraints. Average and Standard deviation values of the objective functions are used to calculate the efficiency of the presented method. The results obtained are compared with the other approaches and it showed that MPSO gives better performance.	en_US
dc.relation.ispartof	60th International Scientific Conference on Information Technology and Management Science of Riga Technical University, ITMS 2019 - Proceedings	en_US
dc.relation.isbasedon	10.1109/ITMS47855.2019.8940635	en_US
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.title	A modified particle swarm optimization algorithm for solving DNA problem	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	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 Biomedical Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
utslib.copyright.status	open_access	*
utslib.copyright.embargo	2021-12-26T00:00:00+1100
pubs.publication-status	Published	en_US

Abstract:

© 2019 IEEE. DNA Sequencing is a complex problem since DNA computation depends on the biochemical reactions of DNA molecules that resulted in an improper or unwanted results. Thus, researchers are focusing to make the molecular computation for the DNA sequences design problem more consistent. Designing of DNA sequencing consists of several difficult and inconsistent designing parameters and typical optimization approaches do not perform well. As a result, a Modified Particle Swarm Optimization Algorithm (MPSO) is proposed to elucidate this problem. Four objective functions that are continuity, similarity, hairpin, and H-measure are used to evaluate this multi-objective problem. Different methods are used to explain DNA problem but to solve it with MPSO the multi-objective problem is converted into a single-objective problem. MPSO is presented to minimize the objective functions subject to two constraints. Average and Standard deviation values of the objective functions are used to calculate the efficiency of the presented method. The results obtained are compared with the other approaches and it showed that MPSO gives better performance.

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http://hdl.handle.net/10453/138168