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：演化算法提供无需梯度的优化方法，这对于难以获取梯度的模型（例如地球科学中的景观演化模型）尤为有利。然而，这类模型有时计算成本高昂，即便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解决这些挑战，但在基于代理模型训练中实现进程间通信较为困难。本文在并行计算架构下，实现了分布式群体优化中基于代理模型的适应度评估估计方法。我们首先在一组基准优化问题上测试该框架，随后将其应用于一个以景观演化模型为特征的地球科学模型。结果表明，该方法在基准函数和Badlands景观演化模型上均取得了令人满意的性能。通过并行计算环境中代理模型的使用，我们在保持优化解精度的同时降低了计算时间。本文的主要贡献在于将基于代理的优化方法应用于地球科学模型，这将有助于未来更深入地理解古气候与地貌学。