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 | |
| Publication status | Published - Oct 2022 |
Bibliographical note
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 | |
| Queen Mary University of London | |
| Engineering and Physical Sciences Research Council | EP/P002625/1 |
| Lloyd's Register Foundation | G0095, EP/R022100/1 |
Keywords
- Basin hopping
- Markov chains
- Rare events
- Skipping sampler
- Stochastic optimisation
Fingerprint
Dive into the research topics of 'Hopping between distant basins'. Together they form a unique fingerprint.Cite this
- APA
- Author
- BIBTEX
- Harvard
- Standard
- RIS
- Vancouver