Parallel multi-objective evolutionary algorithms on graphics processing units

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Most real-life optimization problems or decision-making problems are multi-objective in nature, since they normally have several (possibly conflicting) objectives that must be satisfied at the same time. Multi-Objective Evolutionary Algorithms (MOEAs) have been gaining increasing attention among researchers and practitioners. However, they may execute for a long time for some difficult problems, because several evaluations must be performed. Moreover, the non-dominance checking and the non-dominated selection procedures are also very time consuming. From our experiments, more than 99% of the execution time is used in performing the two procedures. A promising approach to overcome this limitation is to parallelize these algorithms. In this paper, we propose a parallel MOEA on consumer-level Graphics Processing Units (GPU). We perform many experiments on two-objective and three-objective benchmark problems to compare our parallel MOEA with a sequential MOEA and demonstrate that the former is much more efficient than the latter.
Original languageEnglish
Title of host publicationProceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference Late Breaking Papers
EditorsFranz ROTHLAUF
PublisherAssociation for Computing Machinery (ACM)
Number of pages8
ISBN (Print)9781605585055, 9781605583259
Publication statusPublished - Jul 2009
Event11th Annual Genetic and Evolutionary Computation Conference, GECCO-2009 - Montreal, QC, Canada
Duration: 8 Jul 200912 Jul 2009

Publication series

NameProceedings of the 11th Annual Genetic and Evolutionary Computation Conference, GECCO-2009


Conference11th Annual Genetic and Evolutionary Computation Conference, GECCO-2009
Abbreviated titleGECCO09
CityMontreal, QC
OtherAssociation for Computing Machinery

Bibliographical note

Funding Information:
This work is supported by the Lingnan University Direct Grant DR08B2.

Publisher Copyright:
© 2009 ACM.


  • Graphic Process- ing Units
  • Multi-Objective Evolutionary Algorithms
  • Parallel Programming


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