A novel multi-objective optimization method based on an approximation model management technique

In this paper, a novel multi-objective optimization method is suggested based on an approximation model management technique. It is a sequential approximation method, in which a multi-objective optimization with approximation models subject to design variable move limits is iterated until convergence. In each iteration step, the approximation models are constructed by the response surface approximations with the samples which are obtained from the design of experiments, and a Pareto optimal set predicted by the approximations is identified through a multi-objective genetic algorithm. According to the prediction of the approximation models, a move limits updating strategy is employed to determine the design variable move limits for the next iteration. At the end of each iteration step, some uniform distributed points chosen from the predictive Pareto optimal frontier are verified by the high fidelity models and the obtained actual Pareto optimal set is stored in an external archive. The high efficiency of the present method is demonstrated by four different test functions and two engineering applications.