Abstract: Aiming at the large error of optimization results in engineering problems, we proposed a multi-objective particle swarm optimization algorithm based on Kriging models. Firstly, the response information of the Kriging model was used to predict errors,  the prediction errors were introduced into the comparison of Pareto dominance relationship, the selection of global and local leaders, and the process of mutation mechanism. Then, combined with the infilling strategy in the text, the optimization process could quickly and accurately approach the Pareto frontier solution set on the premise of small number of samples . The performance test results show that the proposed algorithm can improve the optimization efficiency and accuracy of complex system models.

Key words: multi-objective optimization, particle swarm algorithm, Kriging model, infilling strategy, mutation pattern