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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
KeywordsGenetic Algorithms
Hopfield Neural Networks
M-Partite Graph Problem
Manufacturing Operation Set Selection
Process Plan Selection
Simulated Annealing
Issue Date1998
PublisherInderscience Publishers. The Journal's web site is located at http://www.inderscience.com/ijmpt
Citation
International Journal Of Materials And Product Technology, 1998, v. 13 n. 3-6, p. 195-213 How to Cite?
AbstractAn 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, a Hopfield neural networks based approach is adopted in this paper. The Liapunov function (energy function) of Hopfield neural networks specially designed for solving the m-partite graph problem is constructed. In order to prohibit Hopfield neural networks from being trapped in their local minima, Simulated Annealing and Genetic Algorithms are utilized and combined with Hopfield neural networks to get a globally optimal solution to the m-partite graph problem. The result of the approaches developed in this paper shows the definitive promise of an optimal solution to the m-partite graph problem compared with that of other currently available algorithms.
Persistent Identifierhttp://hdl.handle.net/10722/155824
ISSN
2015 Impact Factor: 0.365
2015 SCImago Journal Rankings: 0.167
References

 

DC FieldValueLanguage
dc.contributor.authorMing, XGen_US
dc.contributor.authorMak, KLen_US
dc.date.accessioned2012-08-08T08:37:55Z-
dc.date.available2012-08-08T08:37:55Z-
dc.date.issued1998en_US
dc.identifier.citationInternational Journal Of Materials And Product Technology, 1998, v. 13 n. 3-6, p. 195-213en_US
dc.identifier.issn0268-1900en_US
dc.identifier.urihttp://hdl.handle.net/10722/155824-
dc.description.abstractAn 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, a Hopfield neural networks based approach is adopted in this paper. The Liapunov function (energy function) of Hopfield neural networks specially designed for solving the m-partite graph problem is constructed. In order to prohibit Hopfield neural networks from being trapped in their local minima, Simulated Annealing and Genetic Algorithms are utilized and combined with Hopfield neural networks to get a globally optimal solution to the m-partite graph problem. The result of the approaches developed in this paper shows the definitive promise of an 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/ijmpten_US
dc.relation.ispartofInternational Journal of Materials and Product Technologyen_US
dc.subjectGenetic Algorithmsen_US
dc.subjectHopfield Neural Networksen_US
dc.subjectM-Partite Graph Problemen_US
dc.subjectManufacturing Operation Set Selectionen_US
dc.subjectProcess Plan Selectionen_US
dc.subjectSimulated Annealingen_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/IJMPT.1998.036237-
dc.identifier.scopuseid_2-s2.0-0032269722en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-0032269722&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume13en_US
dc.identifier.issue3-6en_US
dc.identifier.spage195en_US
dc.identifier.epage213en_US
dc.publisher.placeUnited Kingdomen_US
dc.identifier.scopusauthoridMing, XG=7005300183en_US
dc.identifier.scopusauthoridMak, KL=7102680226en_US

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