Parameterized complexity analysis and more effective construction methods for ACO algorithms and the Euclidean traveling salesperson problem

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dc.contributor.authorNallaperuma, S.
dc.contributor.authorSutton, A.
dc.contributor.authorNeumann, F.
dc.contributor.conferenceIEEE Congress on Evolutionary Computation (2013 : Cancun, Mexico)
dc.date.issued2013
dc.description.abstractWe propose a new construction procedure for ant colony optimization (ACO) algorithms working on the Euclidean traveling salesperson problem (TSP) that preserves the ordering on the convex hull of the points in the instance. The procedure is inspired by theoretical analyses for simple evolutionary algorithms that are provably more efficient on instances where the number of inner points of the instance is not too large. We integrate the construction procedure into the well-known MaxMin Ant System (MMAS) and empirically show that it leads to more efficient optimization on instances where the number of inner points is not too high.
dc.description.statementofresponsibilitySamadhi Nallaperuma, Andrew M. Sutton, Frank Neumann
dc.identifier.citation2013 IEEE Congress on Evolutionary Computation (CEC): pp.2045-2052
dc.identifier.doi10.1109/CEC.2013.6557810
dc.identifier.isbn9781479904525
dc.identifier.orcidNeumann, F. [0000-0002-2721-3618]
dc.identifier.urihttp://hdl.handle.net/2440/83854
dc.language.isoen
dc.publisherIEEE
dc.publisher.placeUnited States
dc.rights©2013 IEEE
dc.source.urihttps://doi.org/10.1109/cec.2013.6557810
dc.titleParameterized complexity analysis and more effective construction methods for ACO algorithms and the Euclidean traveling salesperson problem
dc.typeConference paper
pubs.publication-statusPublished

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