Optimization of low-loss, high birefringence parameters of a hollow-core anti-resonant fiber with back-propagation neural network assisted hyperplane segmentation algorithm.

In engineering, optimizing parameters often involves computationally expensive tasks, especially when dealing with multi-dimensional variables and multiple performance metrics. This falls under the category of multi-objective black-box optimization. To address this, we propose two optimization algorithms for low and medium-dimensional spaces, incorporating relaxation conditions for hyperplane segmentation.