pythOPT: A problem-solving environment for optimization methods
| dc.contributor.advisor | Spiteri, Raymond | |
| dc.contributor.committeeMember | Eramian, Mark | |
| dc.contributor.committeeMember | Horsch, Michael | |
| dc.contributor.committeeMember | Steele, Tom | |
| dc.creator | Voss, Krzysztof M | |
| dc.creator.orcid | 0000-0002-2961-1193 | |
| dc.date.accessioned | 2017-02-15T21:43:38Z | |
| dc.date.available | 2019-07-30T19:50:26Z | |
| dc.date.created | 2017-02 | |
| dc.date.issued | 2017-02-15 | |
| dc.date.submitted | February 2017 | |
| dc.date.updated | 2017-02-15T21:43:38Z | |
| dc.description.abstract | Optimization is a process of finding the best solutions to problems based on mathematical models. There are numerous methods for solving optimization problems, and there is no method that is superior for all problems. This study focuses on the Particle Swarm Optimization (PSO) family of methods, which is based on the swarm behaviour of biological organisms. These methods are easily adjustable, scalable, and have been proven successful in solving optimization problems. This study examines the performance of nine optimization methods on four sets of problems. The performance analysis of these methods is based on two performance metrics (the win-draw-loss metric and the performance profiles metric) that are used to analyze experimental data. The data are gathered by using each optimization method in multiple configurations to solve four classes of problems. A software package pythOPT was created. It is a problem-solving environment that is comprised of a library, a framework, and a system for benchmarking optimization methods. pythOPT includes code that prepares experiments, executes computations on a distributed system, stores results in a database, analyzes those results, and visualizes analyses. It also includes a framework for building PSO-based methods and a library of benchmark functions used in one of the presented analyses. Using pythOPT, the performance of these nine methods is compared in relation to three parameters: number of available function evaluations, accuracy of solutions, and communication topology. This experiment demonstrates that two methods (SPSO and GCPSO) are superior in finding solutions for the tested classes of problems. Finally, by using pythOPT we can recreate this study and produce similar ones by changing the parameters of an experiment. We can add new methods and evaluate their performances, and this helps in developing new optimization methods. | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.uri | http://hdl.handle.net/10388/7746 | |
| dc.subject | optimization | |
| dc.subject | pso | |
| dc.subject | particle swarm optimization | |
| dc.subject | direct | |
| dc.subject | benchmarking | |
| dc.title | pythOPT: A problem-solving environment for optimization methods | |
| dc.type | Thesis | |
| dc.type.material | text | |
| local.embargo.terms | 2019-07-30 | |
| thesis.degree.department | Computer Science | |
| thesis.degree.discipline | Computer Science | |
| thesis.degree.grantor | University of Saskatchewan | |
| thesis.degree.level | Masters | |
| thesis.degree.name | Master of Science (M.Sc.) |