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dc.contributor.authorTaşgetiren, Fatih
dc.contributor.authorChen, Angela
dc.contributor.authorGençyılmaz, Güneş
dc.contributor.authorGattoufi, Said
dc.date.accessioned2020-03-12T07:57:41Z
dc.date.available2020-03-12T07:57:41Z
dc.date.issued2009
dc.identifier175tr_TR
dc.identifier.isbn978-3-540-92150-9
dc.identifier.issn1860-949X
dc.identifier.urihttps://hdl.handle.net/11413/6308
dc.description.abstractIn a traveling salesman problem, if the set of nodes is divided into clusters for a single node from each cluster to be visited, then the problem is known as the generalized traveling salesman problem (GTSP). Such problem aims to find a tour with minimum cost passing through only a single node from each cluster. In attempt to show how a continuous optimization algorithm can be used to solve a discrete/combinatorial optimization problem, this chapter presents a standard continuous differential evolution algorithm along with a smallest position value (SPV) rule and a unique solution representation to solve the GTSP. The performance of the differential evolution algorithm is tested on a set of benchmark instances with symmetric distances ranging from 51 (11) to 442 (89) nodes (clusters) from the literature. Computational results are presented and compared to a random key genetic algorithm (RKGA) from the literature.
dc.language.isoen_UStr_TR
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/us/*
dc.subjectTraveling Salesman Problem
dc.subjectN-SETS
dc.subjectAlgorithm
dc.subjectNodes
dc.subjectGezgin Satıcı Sorunu
dc.subjectAlgoritma
dc.subjectDüğümler
dc.titleSmallest position value approach
dc.typeBook chaptertr_TR
dc.relation.journalDifferential Evolution a handbook for Global Permutation Based Combinatoral Optimizationtr_TR
local.journal.startpage121tr_TR
local.journal.endpage138tr_TR
dc.identifier.wos000266824900005
dc.identifier.wos266824900005en


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Attribution-NonCommercial-NoDerivs 3.0 United States
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 3.0 United States