关键词: Kriging模型, 物理规划, 代理优化, 多目标优化

Abstract: When dealing with computationally expensive black-box engineering optimization problems, the genetic algorithm is inefficient. In order to improve engineering optimization efficiency, combining Kriging surrogate optimization and physical programming, this paper proposes a multi-objective surrogate optimization algorithm based on Kriging and physical programming. When dealing with multi-objective problems, this paper uses physical programming to convert multi-objective problems into single-objective problems, and then uses Kriging surrogate optimization to solve the problem. Two multi-objective numerical examples and one engineering example are used to verify the proposed algorithm. The results show that the proposed algorithm can find Pareto optimal solutions in accordance with preference settings, and the algorithm is more efficient.

Key words: Kriging model, physical programming, surrogate optimization, multi-objective optimization