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Approaches to Parallelize Pareto Ranking in NSGA-II Algorithm

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Part of the Lecture Notes in Computer Science book series (LNTCS,volume 7204)

Abstract

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.

Keywords

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

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Lančinskas, A., Žilinskas, J. (2012). Approaches to Parallelize Pareto Ranking in NSGA-II Algorithm. In: Wyrzykowski, R., Dongarra, J., Karczewski, K., Waśniewski, J. (eds) Parallel Processing and Applied Mathematics. PPAM 2011. Lecture Notes in Computer Science, vol 7204. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31500-8_38

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  • DOI: https://doi.org/10.1007/978-3-642-31500-8_38

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-31499-5

  • Online ISBN: 978-3-642-31500-8

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