Robust Multiobjective Evolutionary Algorithm Based on Surrogate-Assisted Robust Distance Metric

Robust multiobjective evolutionary algorithms (RMOEAs) aim to obtain robust optimal solutions. However, traditional RMOEAs typically require evaluating a large number of sampling points, which is often impractical in real-world applications due to the high computational cost. In this article, we propose a robust multiobjective evolutionary algorithm based on surrogate-assisted (RMOEA-SA), which incorporates a radial basis function (RBF) surrogate model and a novel robust distance metric (RDM). The proposed algorithm employs the RBF surrogate model to approximate the fitness values of sampling points, thereby significantly reducing the number of function evaluations during the robust optimization process. Furthermore, an RDM assisted by the RBF surrogate model is introduced to measure the robustness of solutions. Besides, the RDM value of each solution is treated as an additional objective, expanding the original objective space, and selection is conducted in this augmented space to achieve a desirable trade-off between robustness and optimality. The experimental results on standard benchmark functions and two real-world application problems demonstrate the superior feasibility and effectiveness of the proposed method compared with several existing algorithms.