Parameter Tuning Experiments of Population-based Algorithms
Independent thesis Basic level (degree of Bachelor), 30 credits / 45 HE creditsStudent thesis
In this study, three different algorithms are implemented to solve thecapacitated vehicle routing problem with and without time windows:ant colony optimization, a genetic algorithm and a genetic algorithmwith self-organizing map. For the capacitated vehicle routing problemthe Augerat et al’s benchmark problems were used and for the capaci-tated vehicle routing problem with time windows the Solomon’sbenchmark problems. All three algorithms were tuned over thirtyinstances per problem with the tuners SPOT and ParamILS. The tuningresults from all instances were combined to the final parameter valuesand tested on a larger set of instances. The test results were used tocompare the algorithms and tuners against each other. The ant colonyoptimization algorithm outperformed the other algorithms on bothproblems when considering all instances. The genetic algorithm withself-organizing map found more best known solutions than any otheralgorithm when using parameters, on the capacitated vehicle routingproblem. The algorithms performed well and several new best knownresults were discovered for the capacitated vehicle routing problem andnew best solutions found by heuristics were discovered for the 100customer Solomon problems. When comparing the tuners they bothworked well and no clear winner emerged.
Place, publisher, year, edition, pages
2011. , 104 p.
Artificial Intelligence, Ant Colony Optimization, Genetic Algorithm, Self-Organizing Map, Capacitated Vehicle Routing Problem, Capacitated Vehicle Routing Problem with Time Windows, SPOT, ParamILS.
IdentifiersURN: urn:nbn:se:miun:diva-13836OAI: oai:DiVA.org:miun-13836DiVA: diva2:419232