Many engineering problems are essentially expensive multi-/many-objective optimization problems, and surrogate-assisted evolutionary algorithms have gained widespread attention in dealing with them. As the objective dimension increases, the error of predicting solutions based on surrogate models accumulates. Existing algorithms do not have strong selection pressure in the candidate solution obtaining and adaptive sampling stages. These make the effectiveness and area of application of the algorithms unsatisfactory. Therefore, this paper proposes a two-risk archive algorithm, which contains a strategy for mining high-risk and low-risk archives and a four-state adaptive sampling criterion. In the candidate solution mining stage, two types of Kriging models are trained, then conservative optimization models and non-conservative optimization models are constructed for model searching, followed by archive selection to obtain more reliable two-risk archives. In the adaptive sampling stage, in order to improve the performance of the algorithms, the proposed criterion considers environmental assessment, demand assessment, and sampling, where the sampling approach involves the improvement of the comprehensive performance in reliable environments, convergence and diversity in controversial environments, and surrogate model uncertainty. Experimental results on numerous benchmark problems show that the proposed algorithm is far superior to seven state-of-the-art algorithms in terms of comprehensive performance.

许多工程问题本质上是昂贵的多目标/多目标优化问题，代理辅助进化算法在处理这些问题时得到了广泛的关注。随着客观维度的增加，基于代理模型的预测解的误差不断累积。现有算法在候选解获取和自适应采样阶段没有很强的选择压力。这些都使得算法的有效性和应用范围不能令人满意。因此，本文提出一种双风险档案算法，该算法包含挖掘高风险和低风险档案的策略以及四状态自适应采样准则。在候选解挖掘阶段，训练两类克里金模型，然后构建保守优化模型和非保守优化模型进行模型搜索，然后进行档案选择，以获得更可靠的二风险档案。在自适应采样阶段，为了提高算法的性能，所提出的准则考虑了环境评估、需求评估和采样，其中采样方法涉及可靠环境中综合性能的提高、争议环境中的收敛性和多样性，以及代理模型的不确定性。对众多基准问题的实验结果表明，该算法在综合性能方面远远优于七种最先进的算法。