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Parallel multi-objective evolutionary algorithms on graphics processing units

Published: 08 July 2009 Publication History

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.

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    cover image ACM Conferences
    GECCO '09: Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
    July 2009
    1760 pages
    ISBN:9781605585055
    DOI:10.1145/1570256
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Published: 08 July 2009

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    Author Tags

    1. graphic processing units
    2. multi-objective evolutionary algorithms
    3. parallel programming

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    GECCO09: Genetic and Evolutionary Computation Conference
    July 8 - 12, 2009
    Québec, Montreal, Canada

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    • (2018)An Efficient Nondominated Sorting Algorithm for Large Number of FrontsIEEE Transactions on Cybernetics10.1109/TCYB.2017.2789158(1-11)Online publication date: 2018
    • (2018)An Approach to Obtain the Upper Bound on the Number of Non-dominated Fronts in a Population2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI)10.1109/ICACCI.2018.8554837(1947-1951)Online publication date: Sep-2018
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