A Parallel Version of SMS-EMOA for Many-Objective Optimization Problems View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2016

AUTHORS

Raquel Hernández Gómez , Carlos A. Coello Coello , Enrique Alba

ABSTRACT

In the last decade, there has been a growing interest in multi-objective evolutionary algorithms that use performance indicators to guide the search. A simple and effective one is the \(\mathcal {S}\)-Metric Selection Evolutionary Multi-Objective Algorithm (SMS-EMOA), which is based on the hypervolume indicator. Even though the maximization of the hypervolume is equivalent to achieving Pareto optimality, its computational cost increases exponentially with the number of objectives, which severely limits its applicability to many-objective optimization problems. In this paper, we present a parallel version of SMS-EMOA, where the execution time is reduced through an asynchronous island model with micro-populations, and diversity is preserved by external archives that are pruned to a fixed size employing a recently created technique based on the Parallel-Coordinates graph. The proposed approach, called \(\mathcal {S}\)-PAMICRO (PArallel MICRo Optimizer based on the \(\mathcal {S}\) metric), is compared to the original SMS-EMOA and another state-of-the-art algorithm (HypE) on the WFG test problems using up to 10 objectives. Our experimental results show that \(\mathcal {S}\)-PAMICRO is a promising alternative that can solve many-objective optimization problems at an affordable computational cost. More... »

PAGES

568-577

References to SciGraph publications

  • 2015. Parallel Multiobjective Evolutionary Algorithms in SPRINGER HANDBOOK OF COMPUTATIONAL INTELLIGENCE
  • 2003. The Measure of Pareto Optima Applications to Multi-objective Metaheuristics in EVOLUTIONARY MULTI-CRITERION OPTIMIZATION
  • 2014-07. A survey on multi-objective evolutionary algorithms for many-objective problems in COMPUTATIONAL OPTIMIZATION AND APPLICATIONS
  • 2007. Pareto-, Aggregation-, and Indicator-Based Methods in Many-Objective Optimization in EVOLUTIONARY MULTI-CRITERION OPTIMIZATION
  • 2004. Indicator-Based Selection in Multiobjective Search in PARALLEL PROBLEM SOLVING FROM NATURE - PPSN VIII
  • 2009-10-21. A Reduced-Cost SMS-EMOA Using Kriging, Self-Adaptation, and Parallelization in MULTIPLE CRITERIA DECISION MAKING FOR SUSTAINABLE ENERGY AND TRANSPORTATION SYSTEMS
  • 2015. A GPU-Based Algorithm for a Faster Hypervolume Contribution Computation in EVOLUTIONARY MULTI-CRITERION OPTIMIZATION
  • 2005. An EMO Algorithm Using the Hypervolume Measure as Selection Criterion in EVOLUTIONARY MULTI-CRITERION OPTIMIZATION
  • Book

    TITLE

    Parallel Problem Solving from Nature – PPSN XIV

    ISBN

    978-3-319-45822-9
    978-3-319-45823-6

    Author Affiliations

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-319-45823-6_53

    DOI

    http://dx.doi.org/10.1007/978-3-319-45823-6_53

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1011089130


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