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. © 2012 Springer-Verlag.
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CITATION STYLE
Lančinskas, A., & Žilinskas, J. (2012). Approaches to parallelize Pareto ranking in NSGA-II algorithm. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7204 LNCS, pp. 371–380). https://doi.org/10.1007/978-3-642-31500-8_38
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