University of SaskatchewanHARVEST
  • Login
  • Submit Your Work
  • About
    • About HARVEST
    • Guidelines
    • Browse
      • All of HARVEST
      • Communities & Collections
      • By Issue Date
      • Authors
      • Titles
      • Subjects
      • This Collection
      • By Issue Date
      • Authors
      • Titles
      • Subjects
    • My Account
      • Login
      JavaScript is disabled for your browser. Some features of this site may not work without it.
      View Item 
      • HARVEST
      • Electronic Theses and Dissertations
      • Graduate Theses and Dissertations
      • View Item
      • HARVEST
      • Electronic Theses and Dissertations
      • Graduate Theses and Dissertations
      • View Item

      pythOPT: A problem-solving environment for optimization methods

      Thumbnail
      View/Open
      VOSS-THESIS-2017.pdf (2.625Mb)
      Date
      2017-02-15
      Author
      Voss, Krzysztof M
      ORCID
      0000-0002-2961-1193
      Type
      Thesis
      Degree Level
      Masters
      Metadata
      Show full item record
      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.
      Degree
      Master of Science (M.Sc.)
      Department
      Computer Science
      Program
      Computer Science
      Supervisor
      Spiteri, Raymond
      Committee
      Eramian, Mark; Horsch, Michael; Steele, Tom
      Copyright Date
      February 2017
      URI
      http://hdl.handle.net/10388/7746
      Subject
      optimization
      pso
      particle swarm optimization
      direct
      benchmarking
      Collections
      • Graduate Theses and Dissertations
      University of Saskatchewan

      University Library

      © University of Saskatchewan
      Contact Us | Disclaimer | Privacy