Traveling Salesman Heuristics

dc.contributor.authorHorton, Shea
dc.contributor.authorMarshall, John
dc.contributor.authorLewis, John Jack
dc.date.accessioned2021-04-23T15:34:14Z
dc.date.available2021-04-23T15:34:14Z
dc.date.issued2021-04-29
dc.descriptionPresented at Fiat Lux Spring 2021.en_US
dc.description.abstractThe traveling salesman problem (TSP) involves trying to find the optimal path to tour a collection of cities, or vertices of a graph, based on their weights. This would seem simple enough with just a few cities, but when a tour needs to be calculated for hundreds or thousands of cities, the run time becomes completely unmanageable. It is for this reason that there are not any perfectly accurate algorithms to find the optimal path in sufficient polynomial time, but rather heuristics that can find near-optimal paths and weights. Our research revolved around finding the differences between the top heuristics for this problem and determining which would be the best for us to implement. We ended up picking Christofide’s heuristic with a 2-opt algorithm on top of that, guaranteeing us, at worst, to be at 1.5x the length of the optimal path.en_US
dc.identifier.urihttp://hdl.handle.net/11416/541
dc.publisherFlorida Southern Collegeen_US
dc.subjectTraveling salesman problemen_US
dc.subjectHeuristic algorithmsen_US
dc.titleTraveling Salesman Heuristicsen_US
dc.typePresentationen_US

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