A noise-resistant infill sampling criterion in surrogate-assisted multi-objective evolutionary algorithms

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.