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.

翻译：进化算法提供了无需梯度的优化方法，这对于难以获取梯度的模型（如地学景观演化模型）十分有利。然而，这类模型有时计算成本高昂，即使采用基于并行计算的分布式群体优化也难以应对。我们可以引入代理辅助优化等高效策略来应对挑战，但实现基于代理模型训练的进程间通信存在困难。本文在并行计算架构上实现了分布式群体优化中基于代理的适应度评估方法。我们首先在基准优化问题集上测试该框架，随后将其应用于包含景观演化模型的地学模型。实验结果表明，该框架在基准函数测试和Badlands景观演化模型上均展现出优异性能。通过并行计算环境下代理模型的引入，我们在保持优化解精度的同时减少了计算时间。本文的主要贡献在于将基于代理的优化方法成功应用于地学模型，这将有助于未来更好地理解古气候与地貌演化。