A comparative study of performance in particle swarm optimization methods with reflection

Takamasa Ohba, Akiko Takahashi, Jun Imai, Shigeyuki Funabiki

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, two kinds of Reflectance-Adjusting PSO (RAPSO) methods that improve the adjustment of the reflectance in the vector reflection PSO are proposed. One is RAPSO using the standard deviation of optimal evaluation values for the latest steps, called RAPSO-OE. The other is RAPSO using the standard deviation of particles' evaluation values in the present step, called RAPSO-PE. The proposed methods are compared with Simple PSO and Taper-off-Reflectance PSO (TRPSO). The validity is shown by benchmark tests, and the proposed method is applied to the optimization problem of electric power leveling systems in rolling mills. The simulation result shows that adjusting reflectance is effective for reducing search time, especially when the optimal solution exists in the edge of problem domain.

Original languageEnglish
Title of host publication2013 IEEE Power and Energy Society General Meeting, PES 2013
DOIs
Publication statusPublished - 2013
Event2013 IEEE Power and Energy Society General Meeting, PES 2013 - Vancouver, BC, Canada
Duration: Jul 21 2013Jul 25 2013

Publication series

NameIEEE Power and Energy Society General Meeting
ISSN (Print)1944-9925
ISSN (Electronic)1944-9933

Other

Other2013 IEEE Power and Energy Society General Meeting, PES 2013
Country/TerritoryCanada
CityVancouver, BC
Period7/21/137/25/13

Keywords

  • adaptively adjusting reflectance
  • benchmark testing
  • meta-heuristics
  • optimization
  • particle swarm optimization

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Nuclear Energy and Engineering
  • Renewable Energy, Sustainability and the Environment
  • Electrical and Electronic Engineering

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