• DocumentCode
    1791851
  • Title

    Comparison between differential evolution and particle swarm optimization algorithms

  • Author

    Dan Zhang ; Bin Wei

  • Author_Institution
    Dept. of Automotive, Univ. of Ontario Inst. of Technol., Oshawa, ON, Canada
  • fYear
    2014
  • fDate
    3-6 Aug. 2014
  • Firstpage
    239
  • Lastpage
    244
  • Abstract
    In this paper, the performance of differential evolution (DE) and particle swarm optimization (PSO) algorithms are compared and evaluated. The comparison is performed on eight benchmark functions f1-f8. New findings have been discovered for the PSO algorithm and the comparison results in this report show that DE generally is better than PSO in term of solution accuracy and robustness in almost all the problems. Generally, from the numerical results and graphic illustrations, we can demonstrate that DE is more efficient and robust compare to PSO, although PSO gives good results in some cases.
  • Keywords
    algorithm theory; particle swarm optimisation; robust control; PSO algorithms; differential evolution; particle swarm optimization algorithms; robustness; Algorithm design and analysis; Benchmark testing; Optimization; Particle swarm optimization; Sociology; Statistics; differential evolution (DE); optimization algorithm; particle swarm optimization (PSO);
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Mechatronics and Automation (ICMA), 2014 IEEE International Conference on
  • Conference_Location
    Tianjin
  • Print_ISBN
    978-1-4799-3978-7
  • Type

    conf

  • DOI
    10.1109/ICMA.2014.6885702
  • Filename
    6885702