dc.contributor |
Universitat Politècnica de Catalunya. Departament de Matemàtica Aplicada I |
dc.contributor |
Serrat Piè, Carles |
dc.contributor |
Juan Pérez, Angel Alejandro |
dc.contributor.author |
Calvet Liñán, Laura |
dc.date |
2014-06 |
dc.identifier.citation |
FME-1046 |
dc.identifier.uri |
http://hdl.handle.net/2099.1/23143 |
dc.language.iso |
eng |
dc.publisher |
Universitat Politècnica de Catalunya |
dc.publisher |
Universitat de Barcelona |
dc.rights |
info:eu-repo/semantics/openAccess |
dc.rights |
http://creativecommons.org/licenses/by-nc-sa/3.0/es/ |
dc.subject |
Àrees temàtiques de la UPC::Matemàtiques i estadística::Estadística matemàtica |
dc.subject |
Experimental design |
dc.subject |
Parameter Fine-tuning of Metaheuristics |
dc.subject |
Design of Experiments |
dc.subject |
Regression Models |
dc.subject |
Multi-Objective Optimization |
dc.subject |
Disseny d'experiments |
dc.subject |
Classificació AMS::62 Statistics::62K Design of experiments |
dc.title |
Statistical methods for parameter fine-tuning of metaheuristics |
dc.type |
info:eu-repo/semantics/masterThesis |
dc.description.abstract |
Metaheuristics are an approximate method widely used to solve many hard optimization problems in a multitude of fields. They depend on a variable number of parameters. Despite the fact that they are usually capable of finding good solutions within a reasonable time, the difficulty in selecting appropriate values for their parameters causes a loss of efficiency, as it normally requires much time, skills and experience. This master degree s thesis provides a survey of the main approaches developed in the last decade to tackle the problem of choosing a good set of parameter values, called the Parameter Setting Problem, and compares them from a methodological point of view focusing on the statistical procedures used so far by the scientific community. This analysis is accompanied by a proposal of a general methodology. The results of applying it to fine-tuning the parameters of a hybrid algorithm, which combines Biased Randomization with the Iterated Local Search metaheuristic, for solving the Multi-depot Vehicle Routing Problem are also reported. The computational experiment shows promising results and the need / suitability of further investigations based on a wider range of statistical learning techniques. Along these same lines, different suggestions for future work are described. In addition, this work highlights the importance of statistics in operations research giving a real-world example. |