Multi-Objective Optimization with Desirability and Morris-Mitchell Criterion
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866910090835001344 |
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| author | Bartz-Beielstein, Thomas Bartz, Eva Hinterleitner, Alexander Leitenmeier, Christoph Hussein, Ihab Abd El |
| author_facet | Bartz-Beielstein, Thomas Bartz, Eva Hinterleitner, Alexander Leitenmeier, Christoph Hussein, Ihab Abd El |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_21989 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Multi-Objective Optimization with Desirability and Morris-Mitchell Criterion Bartz-Beielstein, Thomas Bartz, Eva Hinterleitner, Alexander Leitenmeier, Christoph Hussein, Ihab Abd El Optimization and Control 90C26 I.2.6; G.1.6 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. |
| title | Multi-Objective Optimization with Desirability and Morris-Mitchell Criterion |
| topic | Optimization and Control 90C26 I.2.6; G.1.6 |
| url | https://arxiv.org/abs/2512.21989 |