An efficient cooling algorithm for annealed neural networks with applications to optimization problems

Persistent Link:
http://hdl.handle.net/10150/278008
Title:
An efficient cooling algorithm for annealed neural networks with applications to optimization problems
Author:
Lee, Hyuk-Jae, 1965-
Issue Date:
1991
Publisher:
The University of Arizona.
Rights:
Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
Abstract:
In this thesis we consider an efficient cooling schedule for a mean field annealing (MFA) algorithm. We combine the MFA algorithm with microcanonical simulation (MCS) method and propose a new algorithm called the microcanonical mean field annealing (MCMFA) algorithm. In the proposed algorithm, the cooling speed is controlled by the current temperature so that the amount of computation in MFA can be reduced without a degradation of performance. Unlike that produced by MFA, the solution quality produced by MCMFA is not affected by the choice of the initial temperature. Properties of MCMFA are analyzed and simulated with Hopfield neural networks (HNN). In order to compare MCMFA with MFA, we apply both algorithms to three problems namely, the graph bipartitioning problem, the traveling salesman problem and the weighted matching problem. Simulation results show that MCMFA produces a superior performance to that of MFA.
Type:
text; Thesis-Reproduction (electronic)
Keywords:
Engineering, Electronics and Electrical.; Computer Science.
Degree Name:
M.S.
Degree Level:
masters
Degree Program:
Graduate College
Degree Grantor:
University of Arizona
Advisor:
Louri, Ahmed

Full metadata record

DC FieldValue Language
dc.language.isoen_USen_US
dc.titleAn efficient cooling algorithm for annealed neural networks with applications to optimization problemsen_US
dc.creatorLee, Hyuk-Jae, 1965-en_US
dc.contributor.authorLee, Hyuk-Jae, 1965-en_US
dc.date.issued1991en_US
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en_US
dc.description.abstractIn this thesis we consider an efficient cooling schedule for a mean field annealing (MFA) algorithm. We combine the MFA algorithm with microcanonical simulation (MCS) method and propose a new algorithm called the microcanonical mean field annealing (MCMFA) algorithm. In the proposed algorithm, the cooling speed is controlled by the current temperature so that the amount of computation in MFA can be reduced without a degradation of performance. Unlike that produced by MFA, the solution quality produced by MCMFA is not affected by the choice of the initial temperature. Properties of MCMFA are analyzed and simulated with Hopfield neural networks (HNN). In order to compare MCMFA with MFA, we apply both algorithms to three problems namely, the graph bipartitioning problem, the traveling salesman problem and the weighted matching problem. Simulation results show that MCMFA produces a superior performance to that of MFA.en_US
dc.typetexten_US
dc.typeThesis-Reproduction (electronic)en_US
dc.subjectEngineering, Electronics and Electrical.en_US
dc.subjectComputer Science.en_US
thesis.degree.nameM.S.en_US
thesis.degree.levelmastersen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.advisorLouri, Ahmeden_US
dc.identifier.proquest1346426en_US
dc.identifier.bibrecord.b27226700en_US
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