Advanced Kriging response surface approach for efficient design optimization

Sampling Design Optimization for Space-Time Kriging

Plackett-Burman Design and Response Surface Optimization of Medium Trace Nutrients for Glycolipopeptide Biosurfactant Production

Background: A glycolipopeptide biosurfactant produced by Pseudomonas aeruginosa strain IKW1 reduced the surface tension of fermentation broth from 71.31 to 24.62 dynes/cm at a critical micelle concentration of 20.80 mg/L. The compound proved suitable for applications in emulsion stabilization in food, as well as in cosmetic and pharmaceutical formulations. Methods: In the present study, Placket...

An Efficient Adaptive Sequential Methodology for Expensive Response Surface Optimization

The preset response surface methodology (RSM) designs are commonly used in a wide range of process and design optimization applications. Although they offer ease of implementation and good performance, they are not sufficiently adaptive to reduce the required number of experiments and thus are not cost effective for applications with high cost of experimentation. We propose an efficient adaptiv...

An Approach Based on Enhanced Collaborative Optimization and Kriging Approximation in Multidisciplinary Design Optimization

This paper concentrates on the computational challenge in multidisciplinary design optimization (MDO) and a comprehensive strategy combining enhanced collaborative optimization (ECO) and kriging approximation models is introduced. In this strategy, the computational and organizational advantages of original collaborative optimization (CO) are inherited by ECO, which can satisfy the strengthened...

Modified Bayesian Kriging for noisy response problems and Bayesian confidence-based reliability-based design optimization

The objective of this study is to develop a new modified Bayesian Kriging (MBKG) surrogate modeling method that can be used to carry out confidence-based reliability-based design optimization (RBDO) for problems in which simulation analyses are inherently noisy and standard Kriging approaches fail. The formulation of the MBKG surrogate modeling method is presented, and the full conditional dist...