• DocumentCode
    1192016
  • Title

    Considerations in engineering parallel multiobjective evolutionary algorithms

  • Author

    Van Veldhuizen, David A. ; Zydallis, Jesse B. ; Lamont, Gary B.

  • Volume
    7
  • Issue
    2
  • fYear
    2003
  • fDate
    4/1/2003 12:00:00 AM
  • Firstpage
    144
  • Lastpage
    173
  • Abstract
    Developing multiobjective evolutionary algorithms (MOEAs) involves thoroughly addressing the issues of efficiency and effectiveness. Once convinced of an MOEA´s effectiveness the researcher often desires to reduce execution time and/or resource expenditure, which naturally leads to considering the MOEA´s parallelization. Parallel MOEAs (pMOEAs) or distributed MOEAs are relatively new developments with few associated publications. pMOEA creation is not a simple task, involving analyzing various parallel paradigms and associated parameters. Thus, a thorough discussion of the major parallelized MOEA paradigms is included in this paper and succinct observations are made regarding an analysis of the current literature. Specifically, a previous MOEA notation is extended into the pMOEA domain to enable precise description and identification of various sets of interest. Innovative concepts for pMOEA migration, replacement and niching schemes are discussed, as well as presenting the first known generic pMOEA formulation. Taken together, this paper´s analyses in conjunction with an original pMOEA design serve as a pedagogical framework and example of the necessary process to implement an efficient and effective pMOEA.
  • Keywords
    genetic algorithms; parallel algorithms; Pareto front; Pareto optimality; migration method; optimisation; parallel algorithm; parallel multiobjective evolutionary algorithms; replacement strategy; Computational efficiency; Design optimization; Engineering management; Evolutionary computation; Genetic programming; Military computing; Parallel algorithms; Stochastic processes; Technology management; US Government;
  • fLanguage
    English
  • Journal_Title
    Evolutionary Computation, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1089-778X
  • Type

    jour

  • DOI
    10.1109/TEVC.2003.810751
  • Filename
    1197689