Hopping between distant basins

Maldon Goodridge; John Moriarty; Jure Vogrinc; Alessandro Zocca

doi:10.1007/s10898-022-01153-z

Hopping between distant basins

Maldon Goodridge^*, John Moriarty, Jure Vogrinc, Alessandro Zocca

^*Corresponding author for this work

Mathematics

Research output: Contribution to Journal › Article › Academic › peer-review

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.

Original language	English
Pages (from-to)	465-489
Number of pages	25
Journal	Journal of Global Optimization
Volume	84
Issue number	2
Early online date	25 Apr 2022
DOIs	https://doi.org/10.1007/s10898-022-01153-z
Publication status	Published - Oct 2022

Bibliographical note

Funding Information:
MG was supported by a Queen Mary University of London Principal’s Studentship Award. JM was partially supported by EPSRC grant number EP/P002625/1 and by the Lloyd’s Register Foundation-Alan Turing Institute programme on Data-Centric Engineering under the LRF grant G0095. JV was supported by EPSRC grant number EP/R022100/1.

Publisher Copyright:
© 2022, The Author(s).

Funding

MG was supported by a Queen Mary University of London Principal’s Studentship Award. JM was partially supported by EPSRC grant number EP/P002625/1 and by the Lloyd’s Register Foundation-Alan Turing Institute programme on Data-Centric Engineering under the LRF grant G0095. JV was supported by EPSRC grant number EP/R022100/1.

Funders	Funder number
Lloyd’s Register Foundation-Alan Turing Institute
Lloyd's Register Foundation	G0095
Queen Mary University of London
Engineering and Physical Sciences Research Council	EP/P002625/1, EP/R022100/1

Keywords

Basin hopping
Markov chains
Rare events
Skipping sampler
Stochastic optimisation

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10.1007/s10898-022-01153-z

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title = "Hopping between distant basins",

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.",

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doi = "10.1007/s10898-022-01153-z",

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AU - Moriarty, John

AU - Vogrinc, Jure

AU - Zocca, Alessandro

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N2 - 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.

AB - 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.

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Hopping between distant basins

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