Parallel multi-objective evolutionary algorithms on graphics processing units

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)

Abstract

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)
Pages2515-2522
ISBN (Print)9781605585055
DOIs
Publication statusPublished - Jul 2009
EventThe 2009 Genetic and Evolutionary Computation Conference -
Duration: 1 Jul 20091 Jul 2009

Conference

ConferenceThe 2009 Genetic and Evolutionary Computation Conference
Abbreviated titleGECCO09
Period1/07/091/07/09
OtherAssociation for Computing Machinery

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  • Cite this

    Wong, M. L. (2009). Parallel multi-objective evolutionary algorithms on graphics processing units. In F. ROTHLAUF (Ed.), Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference Late Breaking Papers (pp. 2515-2522). Association for Computing Machinery (ACM). https://doi.org/10.1145/1570256.1570354