AI for Expensive Optimization Problems in Industry

Abstract

The optimization of real-world engineering problems can be a challenging task, owing to the limited understanding of problem characteristics and the high cost of evaluating objectives and constraints in terms of computing time or licenses. This study proposes an AI-assisted optimization pipeline that addresses these challenges by using proxy functions in order to select and optimize the optimization algorithm and its hyperparameters, thereby significantly accelerating the optimization process on the real (expensive) problem. These proxy functions are inexpensive to evaluate and are selected to exhibit similar landscape characteristics as the original problem. To obtain such proxy functions, we adopt an approach, which involves computing Exploratory Landscape Analysis (ELA) features to characterize the problem's landscape. The ELA features are then used to identify an artificial function that replicates the original problem's properties, which can then be employed as a lowcost proxy function for the hyper-parameter optimization of our pipeline. Several real-world industrial applications are discussed as use-case of our proposed approach.