Approaches to Parallelize Pareto Ranking in NSGA-II Algorithm

  • Algirdas Lančinskas
  • Julius Žilinskas
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7204)


In this paper several new approaches to parallelize multi-objective optimization algorithm NSGA-II are proposed, theoretically justified and experimentally evaluated. The proposed strategies are based on the optimization and parallelization of the Pareto ranking part of the algorithm NSGA-II. The speed-up of the proposed strategies have been experimentally investigated and compared with each other as well as with other frequently used strategy on up to 64 processors.


Multi-objective Optimization Pareto Dominance Pareto Ranking Non-dominated Sorting Genetic Algorithm 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Algirdas Lančinskas
    • 1
  • Julius Žilinskas
    • 1
  1. 1.Institute of Mathematics and InformaticsVilnius UniversityVilniusLithuania

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