{"ID":2824995,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.21989","arxiv_id":"2512.21989","title":"Multi-Objective Optimization with Desirability and Morris-Mitchell Criterion","abstract":"Industrial experimental designs frequently lack optimal space-filling properties, rendering them unrepresentative. This study presents a comprehensive methodology to refine existing designs by enhancing coverage quality while optimizing experimental outcomes. We discuss and analyse variants of the Morris-Mitchell criterion to quantify and improve spatial distributions. Based on potential theory, we analyze monotonicity properties and limitations of the Morris-Mitchell criteria. Practically, we implement a multi-objective optimization framework utilizing the Python packages spotdesirability and spotoptim. This framework uses desirability functions to combine surrogate-model predictions with space-filling enhancements into a unified score. Demonstrated through data from a compressor development case study, this approach optimizes performance objectives alongside design coverage. To facilitate implementation, we introduce novel infill-point diagnostics that visually guide the sequential placement of design points. This integrated methodology successfully bridges spatial theory with engineering application, balancing the crucial exploration and exploitation trade-off.","short_abstract":"Industrial experimental designs frequently lack optimal space-filling properties, rendering them unrepresentative. This study presents a comprehensive methodology to refine existing designs by enhancing coverage quality while optimizing experimental outcomes. We discuss and analyse variants of the Morris-Mitchell crite...","url_abs":"https://arxiv.org/abs/2512.21989","url_pdf":"https://arxiv.org/pdf/2512.21989v2","authors":"[\"Thomas Bartz-Beielstein\",\"Eva Bartz\",\"Alexander Hinterleitner\",\"Christoph Leitenmeier\",\"Ihab Abd El Hussein\"]","published":"2025-12-26T11:24:22Z","proceeding":"math.OC","tasks":"[\"math.OC\"]","methods":"[\"LoRA\"]","has_code":false}