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dc.contributor.authorSamanlıoğlu, Funda
dc.contributor.authorFerrell, William G.
dc.contributor.authorKurz, Mary Elizabeth
dc.date.accessioned2021-02-18T20:10:57Z
dc.date.available2021-02-18T20:10:57Z
dc.date.issued2008
dc.identifier.issn03608352
dc.identifier.urihttps://hdl.handle.net/20.500.12469/3944
dc.description.abstractThis paper proposes a methodology to find weakly Pareto optimal solutions to a symmetric multi-objective traveling salesman problem using a memetic random-key genetic algorithm that has been augmented by a 2-opt local search. The methodology uses a "target-vector approach" in which the evaluation function is a weighted Tchebycheff metric with an ideal point and the local search is randomly guided by either a weighted sum of the objectives or a weighted Tchebycheff metric. The memetic algorithm has several advantages including the fact that the random keys representation ensures that feasible tours are maintained during the application of genetic operators. To illustrate the quality of the methodology, experiments are conducted using Euclidean TSP examples and a comparison is made to one example found in the literature.en_US
dc.language.isoEnglishen_US
dc.subjectGenetic algorithmsen_US
dc.subjectHybrid algorithmsen_US
dc.subjectMemetic algorithmsen_US
dc.subjectMulti-objective traveling salesman problemen_US
dc.subjectRandom-keyen_US
dc.titleA memetic random-key genetic algorithm for a symmetric multi-objective traveling salesman problemen_US
dc.typeArticleen_US
dc.identifier.startpage439en_US
dc.identifier.endpage449en_US
dc.relation.journalComputers and Industrial Engineeringen_US
dc.identifier.issue2en_US
dc.identifier.volume55en_US
dc.identifier.doi10.1016/j.cie.2008.01.005en_US
dc.contributor.khasauthorSamanlıoğlu, Fundaen_US


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