Show simple item record

dc.contributor.authorHardas, Shilpa P
dc.date.accessioned2014-03-03T23:22:01Z
dc.date.available2014-03-03T23:22:01Z
dc.date.issued2005-08
dc.identifier.otherhardas_shilpa_p_200508_ms
dc.identifier.urihttp://purl.galileo.usg.edu/uga_etd/hardas_shilpa_p_200508_ms
dc.identifier.urihttp://hdl.handle.net/10724/22673
dc.description.abstractIn this thesis we present a new approach to the Snake-In-the-Box (SIB) problem using Ant Colony Optimization (ACO). ACO refers to a class of algorithms that model the foraging behavior of ants to nd solutions to combinatorial optimization problems. SIB is a well-known problem, that involves nding a Hamiltonian path through a hypercube which follows certain additional constraints. This domain su has been the subject of various search techniques which include genetic algorithms [Potter et al., 1994; Casella, 2005], exhaustive search [Kochut, 1994], mathematical logic [Rajan and Shende, 1999] and iterated local search [Brown, 2005]. After making certain problem speci c customizations a variation on the MIN-MAX Ant System, MMAS SIB, has shown very promising results when applied to the SIB problem. The length of the longest known snake in dimension 8 was matched, using much less computation and time than the best known methods for this problem.
dc.languageeng
dc.publisheruga
dc.rightspublic
dc.subjectAnt Colony Optimization
dc.subjectSnake-In-the-Box
dc.subjectSwarm intelligence
dc.titleAn ant colony approach to the Snake-in-the-Box problem
dc.typeThesis
dc.description.degreeMS
dc.description.departmentArtificial Intelligence
dc.description.majorArtificial Intelligence
dc.description.advisorWalter D. Potter
dc.description.committeeWalter D. Potter
dc.description.committeeKhaled Rasheed
dc.description.committeeRobert W. Robinson


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record