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Dynamic algorithm selection for pareto optimal set approximation

Author

Listed:
  • Ingrida Steponavičė

    (Monash University)

  • Rob J. Hyndman

    (Monash University)

  • Kate Smith-Miles

    (Monash University)

  • Laura Villanova

    (Monash University)

Abstract

This paper presents a meta-algorithm for approximating the Pareto optimal set of costly black-box multiobjective optimization problems given a limited number of objective function evaluations. The key idea is to switch among different algorithms during the optimization search based on the predicted performance of each algorithm at the time. Algorithm performance is modeled using a machine learning technique based on the available information. The predicted best algorithm is then selected to run for a limited number of evaluations. The proposed approach is tested on several benchmark problems and the results are compared against those obtained using any one of the candidate algorithms alone.

Suggested Citation

  • Ingrida Steponavičė & Rob J. Hyndman & Kate Smith-Miles & Laura Villanova, 2017. "Dynamic algorithm selection for pareto optimal set approximation," Journal of Global Optimization, Springer, vol. 67(1), pages 263-282, January.
    • Handle: RePEc:spr:jglopt:v:67:y:2017:i:1:d:10.1007_s10898-016-0420-x
    DOI: 10.1007/s10898-016-0420-x
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    References listed on IDEAS

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    1. Ingrida Steponavice & Rob J Hyndman & Kate Smith-Miles & Laura Villanova, 2014. "Efficient Identification of the Pareto Optimal Set," Monash Econometrics and Business Statistics Working Papers 12/14, Monash University, Department of Econometrics and Business Statistics.
    2. Fuchang Gao & Lixing Han, 2012. "Implementing the Nelder-Mead simplex algorithm with adaptive parameters," Computational Optimization and Applications, Springer, vol. 51(1), pages 259-277, January.
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