• DocumentCode
    117891
  • Title

    Non-dominated sorting environmental adaptation method (NS-EAM)

  • Author

    Nigam, Ritu ; Choudhary, Alok ; Mishra, K.K.

  • Author_Institution
    Comput. Sci. & Eng., ABES Eng. Coll., Ghaziabad, India
  • fYear
    2014
  • fDate
    20-21 Feb. 2014
  • Firstpage
    595
  • Lastpage
    600
  • Abstract
    A modified version of NSGA-II named as NSEAM is proposed to solve multi-objective optimization (MOO) problems, which is better in performance as compared to NSGA-II. In this proposed algorithm a new Bio inspired algorithm (EAM) is used instead of genetic algorithm. Benchmark functions have been used to justify the performance of this newly proposed algorithm. Initially this bio inspired algorithm was created for the purpose of solving single objective optimization (SOO) problems and it had already showed promising results as compared to the currently prevailing evolutionary algorithms. So it was thought to extend this EAM algorithm´s application area over MOO problems. With slight change in the present SOO algorithm, it can be applied in the MOO problems. This proposed algorithm is compared with NSGA-II and Result analysis shows that it is more efficient and converges to the true parito-optimal front very rapidly.
  • Keywords
    Pareto optimisation; evolutionary computation; MOO problems; NS-EAM; NSGA-II; Pareto-optimal front; SOO problems; benchmark functions; bio inspired algorithm; evolutionary algorithms; multiobjective optimization problem; nondominated sorting environmental adaptation method; single objective optimization problems; Evolutionary computation; Genetic algorithms; Genetics; Optimization; Signal processing algorithms; Sociology; Statistics; Environmental Adaptation Algorithm (EAM); Evolutionary Algorithms (EA); Genetic Algorithms (GA);
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Signal Processing and Integrated Networks (SPIN), 2014 International Conference on
  • Conference_Location
    Noida
  • Print_ISBN
    978-1-4799-2865-1
  • Type

    conf

  • DOI
    10.1109/SPIN.2014.6777024
  • Filename
    6777024