Adaptive Latin Hypercube Sampling for a Surrogate-Based Optimization with Artificial Neural Network

A significant number of sample points are often required for surrogate-based optimization when utilizing process simulations to cover the entire system space. This necessity is particularly pronounced in complex simulations or high-dimensional physical experiments, where a large number of sample points is essential. In this study, we have developed an adaptive Latin hypercube sampling (LHS) method that generates additional sample points from areas with the highest output deviations to optimize the required number of samples. The surrogate model used for the optimization problem is artificial neural networks (ANNs). The standard for measuring solution accuracy is the percent error of the optimal solution. The outcomes of the proposed algorithm were compared to those of random sampling for validation. As case studies, we chose three different chemical processes to illustrate problems of varying complexity and numbers of variables. The findings indicate that for all case studies, the proposed LHS optimization algorithm required fewer sample points than random sampling to achieve optimal solutions of similar quality. To extend the application of this methodology, we recommend further applying it to fields beyond chemical engineering and higher-dimensional problems.

Automated summary

In this study, an adaptive Latin hypercube sampling (LHS) method was developed to optimize the required number of samples for surrogate-based optimization. The method generates additional sample points from areas with the highest output deviations. The results demonstrate that this approach requires fewer sample points than random sampling for achieving optimal solutions of similar quality in various chemical processes.