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Surrogate-assisted distributed swarm optimisation for computationally expensive geoscientific models.
- Source :
-
Computational Geosciences . Dec2023, Vol. 27 Issue 6, p939-954. 16p. - Publication Year :
- 2023
-
Abstract
- Evolutionary algorithms provide gradient-free optimisation which is beneficial for models that have difficulty in obtaining gradients; for instance, geoscientific landscape evolution models. However, such models are at times computationally expensive and even distributed swarm-based optimisation with parallel computing struggle. We can incorporate efficient strategies such as surrogate-assisted optimisation to address the challenges; however, implementing inter-process communication for surrogate-based model training is difficult. In this paper, we implement surrogate-based estimation of fitness evaluation in distributed swarm optimisation over a parallel computing architecture. We first test the framework on a set of benchmark optimisation problems and then apply to a geoscientifc model that features landscape evolution model. Our results demonstrate very promising results for benchmark functions and the Badlands landscape evolution model. We obtain a reduction in computationally time while retaining optimisation solution accuracy through the use of surrogates in a parallel computing environment. The major contribution of the paper is in the application of surrogate-based optimisation for geoscientific models which can in the future help in better understanding of paleoclimate and geomorphology. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 14200597
- Volume :
- 27
- Issue :
- 6
- Database :
- Academic Search Index
- Journal :
- Computational Geosciences
- Publication Type :
- Academic Journal
- Accession number :
- 174012353
- Full Text :
- https://doi.org/10.1007/s10596-023-10223-4