A GPU-Based Algorithm for a Faster Hypervolume Contribution Computation View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2015

AUTHORS

Edgar Manoatl Lopez , Luis Miguel Antonio , Carlos A. Coello Coello

ABSTRACT

The hypervolume has become very popular in current multi-objective optimization research. Because of its highly desirable features, it has been used not only as a quality measure for comparing final results of multi-objective evolutionary algorithms (MOEAs), but also as a selection operator (it is, for example, very suitable for many-objective optimization problems). However, it has one serious drawback: computing the exact hypervolume is highly costly. The best known algorithms to compute the hypervolume are polynomial in the number of points, but their cost grows exponentially with the number of objectives. This paper proposes a novel approach which, through the use of Graphics Processing Units (GPUs), computes in a faster way the hypervolume contribution of a point. We develop a highly parallel implementation of our approach and demonstrate its performance when using it within the \(\mathcal {S}\) -Metric Selection Evolutionary Multi-Objective Algorithm (SMS-EMOA). Our results indicate that our proposed approach is able to achieve a significant speed up (of up to 883x) with respect to its sequential counterpart, which allows us to use SMS-EMOA with exact hypervolume calculations, in problems having up to 9 objective functions. More... »

PAGES

80-94

References to SciGraph publications

  • 2003. The Measure of Pareto Optima Applications to Multi-objective Metaheuristics in EVOLUTIONARY MULTI-CRITERION OPTIMIZATION
  • 2004. Indicator-Based Selection in Multiobjective Search in PARALLEL PROBLEM SOLVING FROM NATURE - PPSN VIII
  • 2005. Scalable Test Problems for Evolutionary Multiobjective Optimization in EVOLUTIONARY MULTIOBJECTIVE OPTIMIZATION
  • 2005. A New Analysis of the LebMeasure Algorithm for Calculating Hypervolume in EVOLUTIONARY MULTI-CRITERION OPTIMIZATION
  • 2007. The Hypervolume Indicator Revisited: On the Design of Pareto-compliant Indicators Via Weighted Integration in EVOLUTIONARY MULTI-CRITERION OPTIMIZATION
  • Book

    TITLE

    Evolutionary Multi-Criterion Optimization

    ISBN

    978-3-319-15891-4
    978-3-319-15892-1

    Author Affiliations

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-319-15892-1_6

    DOI

    http://dx.doi.org/10.1007/978-3-319-15892-1_6

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1039579840


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