A Parallel Multi-objective Memetic Algorithm Based on the IGD+ Indicator View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2016

AUTHORS

Edgar Manoatl Lopez , Carlos A. Coello Coello

ABSTRACT

The success of local search techniques in the solution of combinatorial optimization problems has motivated their incorporation into multi-objective evolutionary algorithms, giving rise to the so-called multi-objective memetic algorithms (MOMAs). The main advantage for adopting this sort of hybridization is to speed up convergence to the Pareto front. However, the use of MOMAs introduces new issues, such as how to select the solutions to which the local search will be applied and for how long to run the local search engine (the use of such a local search engine has an extra computational cost). Here, we propose a new MOMA which switches between a hypervolume-based global optimizer and an IGD+-based local search engine. Our proposed local search engine adopts a novel clustering technique based on the IGD+ indicator for splitting the objective space into sub-regions. Since both computing the hypervolume and applying a local search engine are very costly procedures, we propose a GPU-based parallelization of our algorithm. Our preliminary results indicate that our MOMA is able to converge faster than SMS-EMOA to the true Pareto front of multi-objective problems having different degrees of difficulty. More... »

PAGES

473-482

References to SciGraph publications

  • 2007. Test Problems Based on Lamé Superspheres in EVOLUTIONARY MULTI-CRITERION OPTIMIZATION
  • 2005. Scalable Test Problems for Evolutionary Multiobjective Optimization in EVOLUTIONARY MULTIOBJECTIVE OPTIMIZATION
  • 2013. Multi-Objective Differential Evolution on the GPU with C-CUDA in SOFT COMPUTING MODELS IN INDUSTRIAL AND ENVIRONMENTAL APPLICATIONS
  • 2015. Modified Distance Calculation in Generational Distance and Inverted Generational Distance in EVOLUTIONARY MULTI-CRITERION OPTIMIZATION
  • 2013. Data Mining Using Parallel Multi-objective Evolutionary Algorithms on Graphics Processing Units in MASSIVELY PARALLEL EVOLUTIONARY COMPUTATION ON GPGPUS
  • 2005. A Scalable Multi-objective Test Problem Toolkit in EVOLUTIONARY MULTI-CRITERION OPTIMIZATION
  • 2015. A GPU-Based Algorithm for a Faster Hypervolume Contribution Computation 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

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-319-45823-6_44

    DOI

    http://dx.doi.org/10.1007/978-3-319-45823-6_44

    DIMENSIONS

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