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Non-dominated sorting procedure for Pareto dominance ranking on multicore CPU and/or GPU

Author

Listed:
  • G. Ortega

    (University of Almería)

  • E. Filatovas

    (Vilnius University)

  • E. M. Garzón

    (University of Almería)

  • L. G. Casado

    (University of Almería)

Abstract

Evolutionary multi-objective optimization algorithms aim at finding an approximation of the Pareto set. For hard to solve problems with many conflicting objectives, the number of functions evaluations to represent the Pareto front can be large and time consuming. Parallel computing can reduce the wall-clock time of such algorithms. Previous studies tackled the parallelization of a particular evolutionary algorithm. In this research, we focus on improving one of the most time consuming procedures—the non-dominated sorting—, which is used in the state-of-the-art multi-objective genetic algorithms. Here, three parallel versions of the non-dominated sorting procedure are developed: (1) a multicore (based on Pthreads); (2) a Graphic Processing Unit (GPU) (based on CUDA interface); and (3) a hybrid (based on Pthreads and CUDA). The user can select the most suitable option to efficiently compute the non-dominated sorting procedure depending on the available hardware. Results show that the use of GPU computing provides a substantial improvement in terms of performance. The hybrid approach has the best performance when a good load balance is established among cores and GPU.

Suggested Citation

  • G. Ortega & E. Filatovas & E. M. Garzón & L. G. Casado, 2017. "Non-dominated sorting procedure for Pareto dominance ranking on multicore CPU and/or GPU," Journal of Global Optimization, Springer, vol. 69(3), pages 607-627, November.
    • Handle: RePEc:spr:jglopt:v:69:y:2017:i:3:d:10.1007_s10898-016-0468-7
    DOI: 10.1007/s10898-016-0468-7
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    References listed on IDEAS

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    1. Arrondo, Aránzazu Gila & Redondo, Juana L. & Fernández, José & Ortigosa, Pilar M., 2015. "Parallelization of a non-linear multi-objective optimization algorithm: Application to a location problem," Applied Mathematics and Computation, Elsevier, vol. 255(C), pages 114-124.
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    Cited by:

    1. Sumit Mishra & Carlos A. Coello Coello, 2019. "Parallelism in divide-and-conquer non-dominated sorting: a theoretical study considering the PRAM-CREW model," Journal of Heuristics, Springer, vol. 25(3), pages 455-483, June.
    2. J. J. Moreno & G. Ortega & E. Filatovas & J. A. Martínez & E. M. Garzón, 2018. "Improving the performance and energy of Non-Dominated Sorting for evolutionary multiobjective optimization on GPU/CPU platforms," Journal of Global Optimization, Springer, vol. 71(3), pages 631-649, July.
    3. E. Filatovas & O. Kurasova & J. L. Redondo & J. Fernández, 2020. "A reference point-based evolutionary algorithm for approximating regions of interest in multiobjective problems," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(2), pages 402-423, July.

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