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 Publisher Website: 10.1504/IJMPT.1998.036237
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Article: Efficiency of applying Hopfield neural networks with simulated annealing and genetic algorithms for solving mpartite graph problem
Title  Efficiency of applying Hopfield neural networks with simulated annealing and genetic algorithms for solving mpartite graph problem 

Authors  
Keywords  Genetic Algorithms Hopfield Neural Networks MPartite Graph Problem Manufacturing Operation Set Selection Process Plan Selection Simulated Annealing 
Issue Date  1998 
Publisher  Inderscience 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. 36, p. 195213 How to Cite? 
Abstract  An mpartite 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 mpartite 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 mpartite 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 mpartite graph problem. The result of the approaches developed in this paper shows the definitive promise of an optimal solution to the mpartite graph problem compared with that of other currently available algorithms. 
Persistent Identifier  http://hdl.handle.net/10722/155824 
ISSN  2015 Impact Factor: 0.365 2015 SCImago Journal Rankings: 0.167 
References 
DC Field  Value  Language 

dc.contributor.author  Ming, XG  en_US 
dc.contributor.author  Mak, KL  en_US 
dc.date.accessioned  20120808T08:37:55Z   
dc.date.available  20120808T08:37:55Z   
dc.date.issued  1998  en_US 
dc.identifier.citation  International Journal Of Materials And Product Technology, 1998, v. 13 n. 36, p. 195213  en_US 
dc.identifier.issn  02681900  en_US 
dc.identifier.uri  http://hdl.handle.net/10722/155824   
dc.description.abstract  An mpartite 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 mpartite 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 mpartite 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 mpartite graph problem. The result of the approaches developed in this paper shows the definitive promise of an optimal solution to the mpartite graph problem compared with that of other currently available algorithms.  en_US 
dc.language  eng  en_US 
dc.publisher  Inderscience Publishers. The Journal's web site is located at http://www.inderscience.com/ijmpt  en_US 
dc.relation.ispartof  International Journal of Materials and Product Technology  en_US 
dc.subject  Genetic Algorithms  en_US 
dc.subject  Hopfield Neural Networks  en_US 
dc.subject  MPartite Graph Problem  en_US 
dc.subject  Manufacturing Operation Set Selection  en_US 
dc.subject  Process Plan Selection  en_US 
dc.subject  Simulated Annealing  en_US 
dc.title  Efficiency of applying Hopfield neural networks with simulated annealing and genetic algorithms for solving mpartite graph problem  en_US 
dc.type  Article  en_US 
dc.identifier.email  Mak, KL:makkl@hkucc.hku.hk  en_US 
dc.identifier.authority  Mak, KL=rp00154  en_US 
dc.description.nature  link_to_subscribed_fulltext  en_US 
dc.identifier.doi  10.1504/IJMPT.1998.036237   
dc.identifier.scopus  eid_2s2.00032269722  en_US 
dc.relation.references  http://www.scopus.com/mlt/select.url?eid=2s2.00032269722&selection=ref&src=s&origin=recordpage  en_US 
dc.identifier.volume  13  en_US 
dc.identifier.issue  36  en_US 
dc.identifier.spage  195  en_US 
dc.identifier.epage  213  en_US 
dc.publisher.place  United Kingdom  en_US 
dc.identifier.scopusauthorid  Ming, XG=7005300183  en_US 
dc.identifier.scopusauthorid  Mak, KL=7102680226  en_US 