New Feature: Multi-objective global optimization (MOGO)

Process optimization is typically complicated by conflicting design targets, such as productivity and yield. Multi-Objective Global Optimization (MOGO) algorithm utilizes statistical regression models for multi-objective optimization. Kriging is used for estimating functional relationships between design variables and performance indicators . The Pareto front is iteratively approximated by planning new experiments such as to maximize the Expected Hypervolume Improvement (EHVI) as determined from the Kriging models by Markov Chain Monte Carlo (MCMC) sampling.