On Multiple-Objective Nonlinear Optimal Designs

Experiments with multiple objectives form a staple diet of modern scientific research. Deriving optimal designs with multiple objectives is a long-standing challenging problem with few tools available. The few existing approaches cannot provide a satisfied solution in general: either the computation is very expensive or a satisfied solution is not guaranteed. There is need for a general approach which can effectively derive multi-objective optimal designs. A novel algorithm is proposed to address this literature gap. We prove convergence of this algorithm, and show in various examples that the new algorithm can derive the true solutions with high speed.