# Surrogate-assisted distributed swarm optimisation for computationally expensive geoscientific models

arXiv: https://arxiv.org/abs/2201.06843 · HTML: https://mlsanity.com/paper/2201.06843/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 78 references that are in the map.
- First posted on arXiv 18 Jan 2022.

## Builds on (most cited first, 4 of 4)

- [A Literature Survey of Benchmark Functions For Global Optimization Problems](https://mlsanity.com/paper/1308.4008/) (2013)
- [Multi-core parallel tempering Bayeslands for basin and landscape evolution](https://mlsanity.com/paper/1806.10939/) (2018)
- [Bayeslands: A Bayesian inference approach for parameter uncertainty quantification in Badlands](https://mlsanity.com/paper/1805.03696/) (2018)
- [Surrogate-assisted Bayesian inversion for landscape and basin evolution models](https://mlsanity.com/paper/1812.08655/) (2018)

## Abstract (arXiv)

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 struggles. 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 it to a geoscientific model that features a landscape evolution model. Our results demonstrate very promising results for benchmark functions and the Badlands landscape evolution model. We obtain a reduction in computational 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 a better understanding of paleoclimate and geomorphology.