Advances in parallel and distributed computing have enabled efficient
implementation of the distributed swarm and evolutionary algorithms for complex
and computationally expensive models. Evolutionary algorithms provide
gradient-free optimisation which is beneficial for models that do not have such
information available, for instance, geoscientific landscape evolution models.
However, such models are so computationally expensive that even distributed
swarm and evolutionary algorithms with the power of parallel computing
struggle. We need to incorporate efficient strategies such as surrogate
assisted optimisation that further improves their performance; however, this
becomes a challenge given parallel processing and inter-process communication
for implementing surrogate training and prediction. In this paper, we implement
surrogate-based estimation of fitness evaluation in distributed swarm
optimisation over a parallel computing architecture. Our results demonstrate
very promising results for benchmark functions and geoscientific landscape
evolution models. We obtain a reduction in computationally time while retaining
optimisation solution accuracy through the use of surrogates in a parallel
computing environment.

本论文旨在在并行计算结构中实现基于代理的适应度评估，在复杂的地质科学景观进化模型上实现优化解决方案时间的缩短，同时保持优化准确性。