File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Article: A parallel immune optimization algorithm for numeric function optimization

TitleA parallel immune optimization algorithm for numeric function optimization
Authors
KeywordsArtificial immune systems
Function optimization
Immune optimization algorithm
Parallel implementation
Issue Date2009
Citation
Evolutionary Intelligence, 2009, v. 1 n. 3, p. 171-185 How to Cite?
AbstractImmune optimization algorithms show good performance in obtaining optimal solutions especially in dealing with numeric optimization problems where such solutions are often difficult to determine by traditional techniques. This article presents the parallel suppression control algorithm (PSCA), a parallel algorithm for optimization based on artificial immune systems (AIS). PSCA is implemented in a parallel platform where the corresponding population of antibodies is partitioned into subpopulations that are distributed among the processes. Each process executes the immunity-based algorithm for optimizing its subpopulation. In the process of evolving the solutions, the activities of antibodies and the activities of the computation agents are regulated by the general suppression control framework (GSCF) which maintains and controls the interactions between the populations and processes. The proposed algorithm is evaluated with benchmark problems, and its performance is measured and compared with other conventional optimization approaches. © 2008 Springer-Verlag.
Persistent Identifierhttp://hdl.handle.net/10722/58868
ISSN
2015 SCImago Journal Rankings: 0.947
References

 

DC FieldValueLanguage
dc.contributor.authorLau, HYKen_HK
dc.contributor.authorTsang, WWPen_HK
dc.date.accessioned2010-05-31T03:38:27Z-
dc.date.available2010-05-31T03:38:27Z-
dc.date.issued2009en_HK
dc.identifier.citationEvolutionary Intelligence, 2009, v. 1 n. 3, p. 171-185en_HK
dc.identifier.issn1864-5909en_HK
dc.identifier.urihttp://hdl.handle.net/10722/58868-
dc.description.abstractImmune optimization algorithms show good performance in obtaining optimal solutions especially in dealing with numeric optimization problems where such solutions are often difficult to determine by traditional techniques. This article presents the parallel suppression control algorithm (PSCA), a parallel algorithm for optimization based on artificial immune systems (AIS). PSCA is implemented in a parallel platform where the corresponding population of antibodies is partitioned into subpopulations that are distributed among the processes. Each process executes the immunity-based algorithm for optimizing its subpopulation. In the process of evolving the solutions, the activities of antibodies and the activities of the computation agents are regulated by the general suppression control framework (GSCF) which maintains and controls the interactions between the populations and processes. The proposed algorithm is evaluated with benchmark problems, and its performance is measured and compared with other conventional optimization approaches. © 2008 Springer-Verlag.en_HK
dc.languageengen_HK
dc.relation.ispartofEvolutionary Intelligenceen_HK
dc.subjectArtificial immune systemsen_HK
dc.subjectFunction optimizationen_HK
dc.subjectImmune optimization algorithmen_HK
dc.subjectParallel implementationen_HK
dc.titleA parallel immune optimization algorithm for numeric function optimizationen_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.doi10.1007/s12065-008-0014-8en_HK
dc.identifier.scopuseid_2-s2.0-65749098118en_HK
dc.identifier.hkuros160506en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-65749098118&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume1en_HK
dc.identifier.issue3en_HK
dc.identifier.spage171en_HK
dc.identifier.epage185en_HK
dc.identifier.eissn1864-5917-
dc.identifier.scopusauthoridLau, HYK=7201497761en_HK
dc.identifier.scopusauthoridTsang, WWP=35263138900en_HK
dc.identifier.citeulike3652010-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats