Multi-Objective Optimization for Improved Experimental Designs

This paper addresses the limitations of existing experimental designs in industry, which often suffer from poor space-filling properties and bias. It proposes a multi-objective optimization approach that combines surrogate model predictions with a space-filling criterion (intensified Morris-Mitchell) to improve design quality and optimize experimental results. The use of Python packages and a case study from compressor development demonstrates the practical application and effectiveness of the proposed methodology in balancing exploration and exploitation.