  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.
