Performance evaluation of an evolutionary multiobjective optimization based area partitioning and allocation approach

Hassan, M; Liu, D

Performance evaluation of an evolutionary multiobjective optimization based area partitioning and allocation approach

Hassan, M

Liu, D

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Publication Type:: Conference Proceeding
Citation:: IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM, 2018, 2018-July pp. 527 - 532
Issue Date:: 2018-08-30

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Field	Value	Language
dc.contributor.author	Hassan, M https://orcid.org/0000-0002-0824-3272	en_US
dc.contributor.author	Liu, D https://orcid.org/0000-0002-1581-5582	en_US
dc.date.available	2020-09-30T19:05:49Z
dc.date.issued	2018-08-30	en_US
dc.identifier.citation	IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM, 2018, 2018-July pp. 527 - 532	en_US
dc.identifier.isbn	9781538618547	en_US
dc.identifier.uri	http://hdl.handle.net/10453/127718
dc.description.abstract	© 2018 IEEE. An Area Partitioning and Allocation (APA) approach was presented in[1]. The approach focused on optimizing the coverage performance of Autonomous Industrial Robots (AIRs) using multiple conflicting objectives and Voronoi partitioning. However, questions related to the optimality, convergence, and consistency of the Pareto solutions were not studied in details. In this paper, Inverted Generational Distance (IGD) metric is used to verify the convergence of the Pareto front towards Pareto optimal front (PF∗). The consistency in obtaining similar Pareto fronts for independent optimization runs is studied. The computational complexity of the approach with respect to the size of the coverage area and the number of AIRs is also discussed. Two application scenarios are used in this research.	en_US
dc.relation.ispartof	IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM	en_US
dc.relation.isbasedon	10.1109/AIM.2018.8452355	en_US
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Performance evaluation of an evolutionary multiobjective optimization based area partitioning and allocation approach	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2018-July	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	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
utslib.copyright.status	open_access	*
pubs.publication-status	Published	en_US
pubs.volume	2018-July	en_US

Abstract:

© 2018 IEEE. An Area Partitioning and Allocation (APA) approach was presented in[1]. The approach focused on optimizing the coverage performance of Autonomous Industrial Robots (AIRs) using multiple conflicting objectives and Voronoi partitioning. However, questions related to the optimality, convergence, and consistency of the Pareto solutions were not studied in details. In this paper, Inverted Generational Distance (IGD) metric is used to verify the convergence of the Pareto front towards Pareto optimal front (PF∗). The consistency in obtaining similar Pareto fronts for independent optimization runs is studied. The computational complexity of the approach with respect to the size of the coverage area and the number of AIRs is also discussed. Two application scenarios are used in this research.

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http://hdl.handle.net/10453/127718