An efficient pareto set identification approach for multiobjective optimization on black-box functions

Both multiple objectives and computation-intensive black-box functions often exist simultaneously in engineering design problems. Few of existing multiobjective optimization approaches addresses problems with expensive black-box functions. In this paper a new method called the Pareto set pursuing (PSP) method is developed. By developing sampling guidance functions based on approximation models, this approach progressively provides a designer with a rich and evenly distributed set of Pareto optimal points. This work describes PSP procedures in detail. From testing and design application, PSP demonstrates considerable promises in efficiency, accuracy, and robustness. Properties of PSP and differences between PSP and other approximation-based methods are also discussed. It is believed that PSP has a great potential to be a practical tool for multiobjective optimization problems.