Hybrid multi-objective cuckoo search with dynamical local search

Zhang, M; Wang, H; Cui, Z; Chen, J

Hybrid multi-objective cuckoo search with dynamical local search

Zhang, M Wang, H Cui, Z Chen, J

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Publication Type:: Journal Article
Citation:: Memetic Computing, 2018, 10 (2), pp. 199 - 208
Issue Date:: 2018-06-01

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Field	Value	Language
dc.contributor.author	Zhang, M	en_US
dc.contributor.author	Wang, H	en_US
dc.contributor.author	Cui, Z	en_US
dc.contributor.author	Chen, J	en_US
dc.date.issued	2018-06-01	en_US
dc.identifier.citation	Memetic Computing, 2018, 10 (2), pp. 199 - 208	en_US
dc.identifier.issn	1865-9284	en_US
dc.identifier.uri	http://hdl.handle.net/10453/134270
dc.description.abstract	© 2017, Springer-Verlag GmbH Germany. Cuckoo search (CS) is a recently developed meta-heuristic, which has shown good search abilities on many optimization problems. In this paper, we present a hybrid multi-objective CS (HMOCS) for solving multi-objective optimization problems (MOPs). The HMOCS employs the non-dominated sorting procedure and a dynamical local search. The former is helpful to generate Pareto fronts, and the latter focuses on enhance the local search. In order to verify the performance of our approach HMOCS, six well-known benchmark MOPs were used in the experiments. Simulation results show that HMOCS outperforms three other multi-objective algorithms in terms of convergence, spread and distributions.	en_US
dc.relation.ispartof	Memetic Computing	en_US
dc.relation.isbasedon	10.1007/s12293-017-0237-2	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Hybrid multi-objective cuckoo search with dynamical local search	en_US
dc.type	Journal Article
utslib.citation.volume	2	en_US
utslib.citation.volume	10	en_US
utslib.for	0803 Computer Software	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	1004 Medical Biotechnology	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 - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Strength - INEXT - Innovation in IT Services and Applications
utslib.copyright.status	closed_access	*
pubs.issue	2	en_US
pubs.publication-status	Published	en_US
pubs.volume	10	en_US

Abstract:

© 2017, Springer-Verlag GmbH Germany. Cuckoo search (CS) is a recently developed meta-heuristic, which has shown good search abilities on many optimization problems. In this paper, we present a hybrid multi-objective CS (HMOCS) for solving multi-objective optimization problems (MOPs). The HMOCS employs the non-dominated sorting procedure and a dynamical local search. The former is helpful to generate Pareto fronts, and the latter focuses on enhance the local search. In order to verify the performance of our approach HMOCS, six well-known benchmark MOPs were used in the experiments. Simulation results show that HMOCS outperforms three other multi-objective algorithms in terms of convergence, spread and distributions.

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http://hdl.handle.net/10453/134270