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Splitting for Multi-objective Optimization

Author

Listed:
  • Qibin Duan

    (The University of Queensland)

  • Dirk P. Kroese

    (The University of Queensland)

Abstract

We introduce a new multi-objective optimization (MOO) methodology based the splitting technique for rare-event simulation. The method generalizes the elite set selection of the traditional splitting framework, and uses both local and global sampling to sample in the decision space. In addition, an 𝜖-dominance method is employed to maintain good solutions. The algorithm was compared with state-of-the art MOO algorithms using a prevailing set of benchmark problems. Numerical experiments demonstrate that the new algorithm is competitive with the well-established MOO algorithms and that it can outperform the best of them in various cases.

Suggested Citation

  • Qibin Duan & Dirk P. Kroese, 2018. "Splitting for Multi-objective Optimization," Methodology and Computing in Applied Probability, Springer, vol. 20(2), pages 517-533, June.
    • Handle: RePEc:spr:metcap:v:20:y:2018:i:2:d:10.1007_s11009-017-9572-5
    DOI: 10.1007/s11009-017-9572-5
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    References listed on IDEAS

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    1. Zdravko I. Botev & Dirk P. Kroese, 2008. "An Efficient Algorithm for Rare-event Probability Estimation, Combinatorial Optimization, and Counting," Methodology and Computing in Applied Probability, Springer, vol. 10(4), pages 471-505, December.
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