Multi-Objective Optimization for Improved Experimental Designs
Analysis
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.
Key Takeaways
- •Addresses limitations of existing experimental designs.
- •Proposes a multi-objective optimization approach.
- •Combines surrogate model predictions with a space-filling criterion.
- •Demonstrates practical application with Python packages and a case study.
- •Effectively balances exploration and exploitation.
Reference
“The methodology effectively balances the exploration-exploitation trade-off in multi-objective optimization.”