Surrogate-assisted distributed swarm optimisation for computationally expensive geoscientific models

• URL: http://arxiv.org/abs/2201.06843v3
• Date: Mon, 26 Jun 2023 18:39:40 GMT
• Title: Surrogate-assisted distributed swarm optimisation for computationally expensive geoscientific models
• Authors: Rohitash Chandra, Yash Vardhan Sharma
• Abstract summary: 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 the Badlands landscape evolution model. The major contribution of the paper is in the application of surrogate-based optimisation for geoscientific models.
• Score: 1.8627798812601288
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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