Solving the real-world optimization problems often needs a large number of expensive function evaluations (FEs) by using evolutionary algorithms (EAs). To alleviate this difficulty, surrogate-assisted EAs (SAEAs) have attracted more and more attention from academia and industry. However, the existing SAEAs need a large amount of sample points to construct surrogate model within the expected times of FEs, otherwise it cannot achieve satisfactory prediction accuracy. Few SAEAs can reduce the times of expensive FEs while a high-quality surrogate model is constructed using a small number of sample points. In this paper, a novel SAEAs inspired from ensemble learning is proposed. In the proposed algorithm, the small sample date set is divided into multiple subsets, and the surrogate model is trained on each subset. Two new model management strategies based on ensemble learning are applied to global search and local search respectively. Two search methods are cleverly combined to form a high precision surrogate ensemble. In order to verify the performance of the proposed method, we performed comprehensive tests on eight benchmark functions from 10 to 50 dimensions, and compared their result with the five state-of-the-art SAEAs. Experimental results demonstrate that the proposed method shows superior performance in a majority of benchmarks when only a limited computational budget is available. In addition, we apply the proposed algorithm to three real-time optimization problems. The results of each problem are compared with the solutions to verify the effectiveness of the algorithm in solving engineering application problems.

解决现实世界中的优化问题通常需要使用进化算法（EA）进行大量昂贵的功能评估（FE）。为了减轻这一困难，替代辅助EA（SAEA）引起了学术界和工业界越来越多的关注。但是，现有的SAEA需要在FE的预期时间内建立大量的采样点来构建替代模型，否则将无法获得令人满意的预测精度。当使用少量采样点构建高质量的替代模型时，很少有SAEA可以减少昂贵的有限元的时间。在本文中，提出了一种从整体学习中获得启发的新颖的SAEA。在提出的算法中，将小样本日期集划分为多个子集，并在每个子集上训练替代模型。基于整体学习的两种新的模型管理策略分别应用于全局搜索和局部搜索。两种搜索方法巧妙地结合在一起，形成了一个高精度的替代整体。为了验证所提出方法的性能，我们对10至50个维度的八个基准函数进行了全面测试，并将其结果与五个最新的SAEA进行了比较。实验结果表明，在只有有限的计算预算的情况下，该方法在大多数基准测试中均显示出优异的性能。此外，我们将提出的算法应用于三个实时优化问题。将每个问题的结果与解决方案进行比较，以验证算法在解决工程应用问题中的有效性。