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Hopping between distant basins

Author

Listed:
  • Maldon Goodridge

    (Queen Mary University of London)

  • John Moriarty

    (Queen Mary University of London)

  • Jure Vogrinc

    (University of Warwick)

  • Alessandro Zocca

    (Vrije Universiteit Amsterdam)

Abstract

We present and numerically analyse the Basin Hopping with Skipping (BH-S) algorithm for stochastic optimisation. This algorithm replaces the perturbation step of basin hopping (BH) with a so-called skipping mechanism from rare-event sampling. Empirical results on benchmark optimisation surfaces demonstrate that BH-S can improve performance relative to BH by encouraging non-local exploration, that is, by hopping between distant basins.

Suggested Citation

  • Maldon Goodridge & John Moriarty & Jure Vogrinc & Alessandro Zocca, 2022. "Hopping between distant basins," Journal of Global Optimization, Springer, vol. 84(2), pages 465-489, October.
    • Handle: RePEc:spr:jglopt:v:84:y:2022:i:2:d:10.1007_s10898-022-01153-z
    DOI: 10.1007/s10898-022-01153-z
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    References listed on IDEAS

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    1. Pierre Del Moral & Arnaud Doucet & Ajay Jasra, 2006. "Sequential Monte Carlo samplers," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(3), pages 411-436, June.
    2. Reuven Rubinstein, 1999. "The Cross-Entropy Method for Combinatorial and Continuous Optimization," Methodology and Computing in Applied Probability, Springer, vol. 1(2), pages 127-190, September.
    3. Hakon Tjelmeland & Bjorn Kare Hegstad, 2001. "Mode Jumping Proposals in MCMC," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 28(1), pages 205-223, March.
    Full references (including those not matched with items on IDEAS)

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