File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Conference Paper: A chaotic ant colony optimization method for scheduling a single batch-processing machine with non-identical job sizes

TitleA chaotic ant colony optimization method for scheduling a single batch-processing machine with non-identical job sizes
Authors
Cheng, BYChen, HPShao, HXu, RHuang, GQ
Issue Date2008
Citation
2008 Ieee Congress On Evolutionary Computation, Cec 2008, 2008, p. 40-43 How to Cite?
DOI: http://dx.doi.org/10.1109/CEC.2008.4630773
AbstractThe problem of minimizing makespan on a single batch-processing machine with non-identical job sizes is strongly NP-hard. This paper proposes an Ant Colony Optimization (ACO) algorithm with chaotic control to solve the problem. The Metropolis criterion is adopted to select the paths of ants to escape immature convergence. In order to improve the solutions of ACO, a chaotic optimizer is designed and integrated into ACO to reinforce the capacity of global optimization. Batch First Fit is introduced to decode the paths into feasible solutions of the problem. In the experiment, the instances of 24 levels are simulated and the results show that the proposed CACO outperforms Genetic Algorithm and Simulated Annealing on all the instances. © 2008 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/158820
References
References in Scopus

 

DC FieldValueLanguage
dc.contributor.authorCheng, BYen_US
dc.contributor.authorChen, HPen_US
dc.contributor.authorShao, Hen_US
dc.contributor.authorXu, Ren_US
dc.contributor.authorHuang, GQen_US
dc.date.accessioned2012-08-08T09:03:27Z-
dc.date.available2012-08-08T09:03:27Z-
dc.date.issued2008en_US
dc.identifier.citation2008 Ieee Congress On Evolutionary Computation, Cec 2008, 2008, p. 40-43en_US
dc.identifier.urihttp://hdl.handle.net/10722/158820-
dc.description.abstractThe problem of minimizing makespan on a single batch-processing machine with non-identical job sizes is strongly NP-hard. This paper proposes an Ant Colony Optimization (ACO) algorithm with chaotic control to solve the problem. The Metropolis criterion is adopted to select the paths of ants to escape immature convergence. In order to improve the solutions of ACO, a chaotic optimizer is designed and integrated into ACO to reinforce the capacity of global optimization. Batch First Fit is introduced to decode the paths into feasible solutions of the problem. In the experiment, the instances of 24 levels are simulated and the results show that the proposed CACO outperforms Genetic Algorithm and Simulated Annealing on all the instances. © 2008 IEEE.en_US
dc.languageengen_US
dc.relation.ispartof2008 IEEE Congress on Evolutionary Computation, CEC 2008en_US
dc.titleA chaotic ant colony optimization method for scheduling a single batch-processing machine with non-identical job sizesen_US
dc.typeConference_Paperen_US
dc.identifier.emailHuang, GQ:gqhuang@hkucc.hku.hken_US
dc.identifier.authorityHuang, GQ=rp00118en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1109/CEC.2008.4630773en_US
dc.identifier.scopuseid_2-s2.0-55749107925en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-55749107925&selection=ref&src=s&origin=recordpageen_US
dc.identifier.spage40en_US
dc.identifier.epage43en_US
dc.identifier.scopusauthoridCheng, BY=24721115700en_US
dc.identifier.scopusauthoridChen, HP=10045046900en_US
dc.identifier.scopusauthoridShao, H=35775669900en_US
dc.identifier.scopusauthoridXu, R=7402813941en_US
dc.identifier.scopusauthoridHuang, GQ=7403425048en_US

Export via OAI-PMH Interface in XML Formats

OR

Export to Other Non-XML Formats