Surrogate Based Optimization

• development of a new metamodeling technique based on the use ensemble of metamodels with optimized weight factors (Paper)
• investigating various approaches for constructing an ensemble of metamodels using local measures (Paper)
• development of a new methodology for optimizing the shape parameters of radial basis functions (Paper)
• simultaneous optimization of shape parameters and weight factors in ensemble of radial basis functions (Paper)
• exploring effects of the correlation model, trend model and number of training points on the accuracy of Kriging models (Paper)
• investigating effect of error metrics on optimum weight factor selection for ensemble of metamodels (Paper)

Review Paper

• A survey of machine learning techniques in structural and multidisciplinary optimization (Paper)