Taking the human out of decomposition-based optimization via artificial intelligence: Part II. Learning to initialize

TL;DR

This paper introduces a machine learning method to optimize the initialization of decomposition algorithms, significantly reducing solution times in large-scale process systems engineering problems.

Contribution

It presents a novel approach using active and supervised learning to predict optimal initialization parameters, improving efficiency of decomposition-based optimization methods.

Findings

Significant reduction in computational time achieved.

Active learning reduces data requirements for training.

Effective prediction of optimal initialization improves algorithm performance.

Abstract

The repeated solution of large-scale optimization problems arises frequently in process systems engineering tasks. Decomposition-based solution methods have been widely used to reduce the corresponding computational time, yet their implementation has multiple steps that are difficult to configure. We propose a machine learning approach to learn the optimal initialization of such algorithms which minimizes the computational time. Active and supervised learning is used to learn a surrogate model that predicts the computational performance for a given initialization. We apply this approach to the initialization of Generalized Benders Decomposition for the solution of mixed integer model predictive control problems. The surrogate models are used to find the optimal number of initial cuts that should be added in the master problem. The results show that the proposed approach can lead to a significant reduction in solution time, and active learning can reduce the data required for learning.