Bayesian Guided Pattern Search for Robust Local Optimization

Optimization for complex systems in engineering often involves the use of expensive computer simulation. By combining statistical emulation using treed Gaussian processes with pattern search optimization, we are able to perform robust local optimization more efficiently and effectively than when using either method alone. Our approach is based on the augmentation of local search patterns with location sets generated through improvement prediction over the input space. We further develop a computational framework for asynchronous parallel implementation of the optimization algorithm. We demonstrate our methods on two standard test problems and our motivating example of calibrating a circuit device simulator.