Show simple item record

dc.contributor.authorOrth, John
dc.date.accessioned2012-03-02T14:16:28Z
dc.date.available2012-03-02T14:16:28Z
dc.date.issued2012-03-02
dc.identifier.urihttp://hdl.handle.net/10464/3929
dc.description.abstractThis thesis introduces the Salmon Algorithm, a search meta-heuristic which can be used for a variety of combinatorial optimization problems. This algorithm is loosely based on the path finding behaviour of salmon swimming upstream to spawn. There are a number of tunable parameters in the algorithm, so experiments were conducted to find the optimum parameter settings for different search spaces. The algorithm was tested on one instance of the Traveling Salesman Problem and found to have superior performance to an Ant Colony Algorithm and a Genetic Algorithm. It was then tested on three coding theory problems - optimal edit codes, optimal Hamming distance codes, and optimal covering codes. The algorithm produced improvements on the best known values for five of six of the test cases using edit codes. It matched the best known results on four out of seven of the Hamming codes as well as three out of three of the covering codes. The results suggest the Salmon Algorithm is competitive with established guided random search techniques, and may be superior in some search spaces.en_US
dc.language.isoengen_US
dc.publisherBrock Universityen_US
dc.subjectcombinatorial optimizationen_US
dc.subjectcoding theoryen_US
dc.subjectsearch metaheuristicsen_US
dc.titleThe Salmon Algorithm - A New Population Based Search Metaheuristicen_US
dc.typeElectronic Thesis or Dissertationen_US
dc.degree.nameM.Sc. Computer Scienceen_US
dc.degree.levelMastersen_US
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.degree.disciplineFaculty of Mathematics and Scienceen_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record