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      Step-Optimized Particle Swarm Optimization

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      SCHOENE-THESIS.pdf (1.520Mb)
      Date
      2011-10-02
      Author
      Schoene, Thomas
      Type
      Thesis
      Degree Level
      Masters
      Metadata
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      Abstract
      Particle swarm optimization (PSO) is widely used in industrial and academic research to solve optimization problems. Recent developments of PSO show a direction towards adaptive PSO (APSO). APSO changes its behaviour during the optimization process based on information gathered at each iteration. It has been shown that APSO is able to solve a wide range of difficult optimization problems efficiently and effectively. In classical PSO, all parameters are fixed for the entire swarm. In particular, all particles share the same settings of their velocity weights. We propose four APSO variants in which every particle has its own velocity weights. We use PSO to optimize the settings of the velocity weights of every particle at every iteration, thereby creating a step-optimized PSO (SOPSO). We implement four known PSO variants (global best PSO, decreasing weight PSO, time-varying acceleration coefficients PSO, and guaranteed convergence PSO) and four proposed APSO variants (SOPSO, moving bounds SOPSO, repulsive SOPSO, and moving bound repulsive SOPSO) in a PSO software package. The PSO software package is used to compare the performance of the PSO and APSO variants on 22 benchmark problems. Test results show that the proposed APSO variants outperform the known PSO variants on difficult optimization problems that require large numbers of function evaluations for their solution. This suggests that the SOPSO strategy of optimizing the settings of the velocity weights of every particle improves the robustness and performance of PSO.
      Degree
      Master of Science (M.Sc.)
      Department
      Computer Science
      Program
      Computer Science
      Supervisor
      Ludwig, Simone A.; Spiteri, Raymond J.
      Committee
      Eramian, Mark G.; McQuillan, Ian; Dinh, Anh V.
      Copyright Date
      August 2011
      URI
      http://hdl.handle.net/10388/ETD-2011-08-90
      Subject
      step-optimized particle swarm optimization
      adaptive particle swarm optimization
      particle swarm optimization
      SOPSO
      APSO
      PSO
      optimization
      velocity weights
      step-optimized
      optimized
      adaptive
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      • Graduate Theses and Dissertations

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