Most existing surrogate-assisted multi-objective evolutionary algorithms are susceptible to the noise, since the noise affects both the approximation performance of objective surrogate models and the selection accuracy. Therefore, the keys to these algorithms are how to reduce the noise impact without additional function evaluations burden and how to maximize the noise immunity of the algorithms. In this work, a noise-resistant surrogate-assisted multi-objective evolutionary algorithm is proposed to solve the noisy expensive optimization problems. In the proposed algorithm, a novel classification-based noise handling method is used to reduce the noise impact without additional function evaluations burden before optimization. In the optimization process, the denoised data and model selection strategy are used to construct appropriate objective surrogate models to assist in generating promising candidates. Then, a noise-resistant infill sampling criterion considers convergence, diversity, and model uncertainty to select the most potential individual from candidates for re-evaluation. The experimental results on a series of expensive test problems with additive noise have demonstrated the competitiveness of the proposed algorithm against the other comparative algorithms.

中文翻译：

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代理辅助多目标进化算法中的抗噪声填充采样准则

大多数现有的代理辅助多目标进化算法容易受到噪声的影响，因为噪声既影响目标代理模型的逼近性能，又影响选择精度。因此，这些算法的关键是如何在不增加额外函数评估负担的情况下降低噪声影响，以及如何最大化算法的抗噪声能力。在这项工作中，提出了一种抗噪声代理辅助多目标进化算法来解决噪声昂贵的优化问题。在所提出的算法中，使用一种新颖的基于分类的噪声处理方法来减少噪声影响，而无需在优化之前增加额外的功能评估负担。在优化过程中，使用去噪数据和模型选择策略来构建适当的客观替代模型，以帮助生成有希望的候选模型。然后，抗噪声填充采样标准考虑收敛性、多样性和模型不确定性，从候选者中选择最有潜力的个体进行重新评估。一系列昂贵的加性噪声​​测试问题的实验结果证明了所提出的算法相对于其他对比算法的竞争力。