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Article: Efficiency of applying Hopfield neural networks with simulated annealing and genetic algorithms for solving m-partite graph problem

TitleEfficiency of applying Hopfield neural networks with simulated annealing and genetic algorithms for solving m-partite graph problem
Authors
Issue Date1999
PublisherInderscience Publishers. The Journal's web site is located at http://www.inderscience.com/ijcat
Citation
International Journal Of Computer Applications In Technology, 1999, v. 12 n. 6, p. 339-348 How to Cite?
AbstractA m-partite graph is defined as a graph that consists of m nodes each of which contains a set of elements, and the arcs connecting elements from different nodes. Each element in this graph comprises its specific attributes such as cost and resources. The weighted values of arcs represent the dissimilarities of resources between elements from different nodes. The m-partite graph problem is defined as selecting exactly one representative from a set of elements for each node in such a way that the sum of both the costs of the selected elements and their dissimilarities is minimized. In order to solve such a problem, Hopfield neural networks based approach is adopted in this paper. The Liapunov function (energy function) of Hopfield neural networks specially designed for solving m-partite graph problem is constructed. In order to prohibit Hopfield neural networks from becoming trapped in their local minima, simulated annealing and genetic algorithms are thus utilized and combined with Hopfield neural networks to get globally optimal solution to m-partite graph problem. The result of the approaches developed in this paper shows the definitive promise for leading to the optimal solution to the m-partite graph problem compared with that of other currently available algorithms.
Persistent Identifierhttp://hdl.handle.net/10722/155835
ISSN
2000 Impact Factor: 0.041
2015 SCImago Journal Rankings: 0.309
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorMing, XGen_US
dc.contributor.authorMak, KLen_US
dc.date.accessioned2012-08-08T08:37:58Z-
dc.date.available2012-08-08T08:37:58Z-
dc.date.issued1999en_US
dc.identifier.citationInternational Journal Of Computer Applications In Technology, 1999, v. 12 n. 6, p. 339-348en_US
dc.identifier.issn0952-8091en_US
dc.identifier.urihttp://hdl.handle.net/10722/155835-
dc.description.abstractA m-partite graph is defined as a graph that consists of m nodes each of which contains a set of elements, and the arcs connecting elements from different nodes. Each element in this graph comprises its specific attributes such as cost and resources. The weighted values of arcs represent the dissimilarities of resources between elements from different nodes. The m-partite graph problem is defined as selecting exactly one representative from a set of elements for each node in such a way that the sum of both the costs of the selected elements and their dissimilarities is minimized. In order to solve such a problem, Hopfield neural networks based approach is adopted in this paper. The Liapunov function (energy function) of Hopfield neural networks specially designed for solving m-partite graph problem is constructed. In order to prohibit Hopfield neural networks from becoming trapped in their local minima, simulated annealing and genetic algorithms are thus utilized and combined with Hopfield neural networks to get globally optimal solution to m-partite graph problem. The result of the approaches developed in this paper shows the definitive promise for leading to the optimal solution to the m-partite graph problem compared with that of other currently available algorithms.en_US
dc.languageengen_US
dc.publisherInderscience Publishers. The Journal's web site is located at http://www.inderscience.com/ijcaten_US
dc.relation.ispartofInternational Journal of Computer Applications in Technologyen_US
dc.titleEfficiency of applying Hopfield neural networks with simulated annealing and genetic algorithms for solving m-partite graph problemen_US
dc.typeArticleen_US
dc.identifier.emailMak, KL:makkl@hkucc.hku.hken_US
dc.identifier.authorityMak, KL=rp00154en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1504/IJCAT.1999.000217-
dc.identifier.scopuseid_2-s2.0-0033344308en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-0033344308&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume12en_US
dc.identifier.issue6en_US
dc.identifier.spage339en_US
dc.identifier.epage348en_US
dc.identifier.isiWOS:000084588000005-
dc.publisher.placeUnited Kingdomen_US
dc.identifier.scopusauthoridMing, XG=7005300183en_US
dc.identifier.scopusauthoridMak, KL=7102680226en_US

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