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Article: Immunity-based hybrid evolutionary algorithm for multi-objective optimization in global container repositioning

TitleImmunity-based hybrid evolutionary algorithm for multi-objective optimization in global container repositioning
Authors
KeywordsArtificial immune system
Container repositioning
Evolutionary algorithms
Optimization
Issue Date2009
PublisherElsevier Ltd. The Journal's web site is located at http://www.elsevier.com/locate/engappai
Citation
Engineering Applications Of Artificial Intelligence, 2009, v. 22 n. 6, p. 842-854 How to Cite?
AbstractThe development of evolutionary algorithms for optimization has always been a stimulating and growing research area with an increasing demand in using them to solve complex industrial optimization problems. A novel immunity-based hybrid evolutionary algorithm known as Hybrid Artificial Immune Systems (HAIS) for solving both unconstrained and constrained multi-objective optimization problems is developed in this research. The algorithm adopts the clonal selection and immune suppression theories, with a sorting scheme featuring uniform crossover, multi-point mutation, non-dominance and crowding distance sorting to attain the Pareto optimal front in an efficient manner. The proposed algorithm was verified with nine benchmarking functions on its global optimal search ability as well as compared with four optimization algorithms to assess its diversity and spread. Sensitivity analysis was also carried out to investigate the selection of key parameters of the algorithm. It is found that the developed immunity-based hybrid evolutionary algorithm provides a useful means for solving optimization problems and has successfully applied to the problem of global repositioning of containers, which is one of a constrained multi-objective optimization problem. The developed HAIS will assist shipping liners on timely decision making and planning of container repositioning operations in global container transportation business in an optimized and cost effective manner. © 2008 Elsevier Ltd. All rights reserved.
Persistent Identifierhttp://hdl.handle.net/10722/58879
ISSN
2015 Impact Factor: 2.368
2015 SCImago Journal Rankings: 1.371
ISI Accession Number ID
Funding AgencyGrant Number
Research Grant Council of the Hong Kong Special Administrative RegionHKU7142/06E
Funding Information:

The work described in this paper was partly supported by the Research Grant Council of the Hong Kong Special Administrative Region, PRC under the GRF Project no. HKU7142/06E.

References

 

DC FieldValueLanguage
dc.contributor.authorWong, EYCen_HK
dc.contributor.authorYeung, HSCen_HK
dc.contributor.authorLau, HYKen_HK
dc.date.accessioned2010-05-31T03:38:39Z-
dc.date.available2010-05-31T03:38:39Z-
dc.date.issued2009en_HK
dc.identifier.citationEngineering Applications Of Artificial Intelligence, 2009, v. 22 n. 6, p. 842-854en_HK
dc.identifier.issn0952-1976en_HK
dc.identifier.urihttp://hdl.handle.net/10722/58879-
dc.description.abstractThe development of evolutionary algorithms for optimization has always been a stimulating and growing research area with an increasing demand in using them to solve complex industrial optimization problems. A novel immunity-based hybrid evolutionary algorithm known as Hybrid Artificial Immune Systems (HAIS) for solving both unconstrained and constrained multi-objective optimization problems is developed in this research. The algorithm adopts the clonal selection and immune suppression theories, with a sorting scheme featuring uniform crossover, multi-point mutation, non-dominance and crowding distance sorting to attain the Pareto optimal front in an efficient manner. The proposed algorithm was verified with nine benchmarking functions on its global optimal search ability as well as compared with four optimization algorithms to assess its diversity and spread. Sensitivity analysis was also carried out to investigate the selection of key parameters of the algorithm. It is found that the developed immunity-based hybrid evolutionary algorithm provides a useful means for solving optimization problems and has successfully applied to the problem of global repositioning of containers, which is one of a constrained multi-objective optimization problem. The developed HAIS will assist shipping liners on timely decision making and planning of container repositioning operations in global container transportation business in an optimized and cost effective manner. © 2008 Elsevier Ltd. All rights reserved.en_HK
dc.languageengen_HK
dc.publisherElsevier Ltd. The Journal's web site is located at http://www.elsevier.com/locate/engappaien_HK
dc.relation.ispartofEngineering Applications of Artificial Intelligenceen_HK
dc.subjectArtificial immune systemen_HK
dc.subjectContainer repositioningen_HK
dc.subjectEvolutionary algorithmsen_HK
dc.subjectOptimizationen_HK
dc.titleImmunity-based hybrid evolutionary algorithm for multi-objective optimization in global container repositioningen_HK
dc.typeArticleen_HK
dc.identifier.emailLau, HYK:hyklau@hkucc.hku.hken_HK
dc.identifier.authorityLau, HYK=rp00137en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.engappai.2008.10.010en_HK
dc.identifier.scopuseid_2-s2.0-70449522375en_HK
dc.identifier.hkuros160512en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-70449522375&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume22en_HK
dc.identifier.issue6en_HK
dc.identifier.spage842en_HK
dc.identifier.epage854en_HK
dc.identifier.isiWOS:000268819800003-
dc.publisher.placeUnited Kingdomen_HK
dc.identifier.scopusauthoridWong, EYC=35168258300en_HK
dc.identifier.scopusauthoridYeung, HSC=35168297400en_HK
dc.identifier.scopusauthoridLau, HYK=7201497761en_HK
dc.identifier.citeulike5475469-

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