Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/68736
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dc.titleA columnar competitive model with simulated annealing for solving combinatorial optimization problems
dc.contributor.authorEu, J.T.
dc.contributor.authorHuajin, T.
dc.contributor.authorKay, C.T.
dc.date.accessioned2014-06-19T02:52:38Z
dc.date.available2014-06-19T02:52:38Z
dc.date.issued2006
dc.identifier.citationEu, J.T.,Huajin, T.,Kay, C.T. (2006). A columnar competitive model with simulated annealing for solving combinatorial optimization problems. IEEE International Conference on Neural Networks - Conference Proceedings : 3254-3259. ScholarBank@NUS Repository.
dc.identifier.isbn0780394909
dc.identifier.issn10987576
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/68736
dc.description.abstractOne of the major drawbacks of the Hopfield network is that when it is applied to certain polytopes of combinatorial problems, such as the traveling salesman problem (TSP), the obtained solutions are often invalid, requiring numerous trial-and-error setting of the network parameters thus resulting in low-computation efficiency. With this in mind, this article presents a columnar competitive model (CCM) which incorporates a winner-takes-all (WTA) learning rule for solving the TSP. Theoretical analysis for the convergence of the CCM shows that the competitive computational neural network guarantees the convergence of the network to valid states and avoids the tedious procedure of determining the penalty parameters. In addition, its intrinsic competitive learning mechanism enables a fast and effective evolving of the network. Simulation results illustrate that the competitive model offers more and better valid solutions as compared to the original Hopfield network. © 2006 IEEE.
dc.sourceScopus
dc.subjectCombinatorial optimization
dc.subjectCompetitive learning
dc.subjectSimulated annealing
dc.subjectTraveling salesman problem
dc.typeConference Paper
dc.contributor.departmentELECTRICAL & COMPUTER ENGINEERING
dc.description.sourcetitleIEEE International Conference on Neural Networks - Conference Proceedings
dc.description.page3254-3259
dc.description.codenICNNF
dc.identifier.isiutNOT_IN_WOS
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