AI for Expensive Optimization Problems in Industry
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The optimization of real-world engineering problems can be a challenging task, due to the limited understanding of problem characteristics and the high computational cost of objectives and constraints. This study proposes an AI-assisted optimization pipeline that addresses these challenges by using proxy functions in order to select and optimize an optimization algorithm and its hyper-parameters. It thereby significantly accelerates the optimization process on the real (expensive) problem. To obtain such proxy functions Exploratory Landscape Analysis (ELA) features are used to characterize the problem’s landscape. The ELA features are then used to identify an artificial function that replicates the original problem’s properties.